Large Language Models (LLMs) are now widely used for query reformulation and expansion in Information Retrieval, with many studies reporting substantial effectiveness gains. However, these results are typically obtained under heterogeneous experimental conditions, making it difficult to assess which findings are reproducible and which depend on specific implementation choices. In this work, we present a systematic reproducibility and comparative study of ten representative LLM-based query reformulation methods under a unified and strictly controlled experimental framework. We evaluate methods across two architectural LLM families at two parameter scales, three retrieval paradigms (lexical, learned sparse, and dense), and nine benchmark datasets spanning TREC Deep Learning and BEIR. Our results show that reformulation gains are strongly conditioned on the retrieval paradigm, that improvements observed under lexical retrieval do not consistently transfer to neural retrievers, and that larger LLMs do not uniformly yield better downstream performance. These findings clarify the stability and limits of reported gains in prior work. To enable transparent replication and ongoing comparison, we release all prompts, configurations, evaluation scripts, and run files through QueryGym, an open-source reformulation toolkit with a public leaderboard.\footnote{https://leaderboard.querygym.com}

Second-stage neural rankers are commonly applied with a fixed re-ranking depth, assuming that retrieval effectiveness saturates as depth increases. Prior research has questioned the assumption that increasing the re-ranking depth yields linear performance gains, further suggesting that optimal re-ranking depth varies considerably from query to query. In the past, studying such phenomena was methodologically difficult given the scale of manual annotation required. With the advent of LLM-based relevance judgments we can now more easily undertake such studies, in this case to pinpoint the optimal re-ranking depth on a per-query basis. Using dense LLM-based relevance judgments over a typical re-ranking pipeline, we show that many queries exhibit a well-defined optimal re-ranking depth, beyond which effectiveness stagnates or degrades. We formulate re-ranking depth as a query-specific property and study whether it can be predicted a priori from first-stage retrieval characteristics. Through a large-scale analysis, we find that most standard query performance prediction (QPP) methods are ineffective for this task. In contrast, a predictor derived from LLM-assessed first-stage ranking quality, which we term IR-DCG@10, can reduce the average re-ranking depth by up to a factor of 3 while preserving overall effectiveness, depending on the specific first- and second-stage rankers used. Under oracle selection of optimal depths, we further show that retrieval effectiveness can improve by more than 7% while reducing the average re-ranking depth by a factor of 5 on the MSMARCO DEV collection. Given our promising preliminary findings, we would encourage the use of automatic judgments to facilitate research otherwise infeasible under manual annotation. Towards this point, we release relevance judgments, our codebase, and experimental artefacts to support reproducibility and further research.

Many readers today struggle to assess the trustworthiness of online news because reliable reporting coexists with misinformation. The TREC 2025 DRAGUN (Detection, Retrieval, and Augmented Generation for Understanding News) Track provided a venue for researchers to develop and evaluate assistive RAG systems that support readers' news trustworthiness assessment by producing reader-oriented, well-attributed reports. As the organizers of the DRAGUN track, we describe the resources that we have newly developed to allow for the reuse of the track's tasks. The track had two tasks: (Task 1) Question Generation, producing 10 ranked investigative questions; and (Task 2, the main task) Report Generation, producing a 250-word report grounded in the MS MARCO V2.1 Segmented Corpus. As part of the track's evaluation, we had TREC assessors create importance-weighted rubrics of questions with expected short answers for 30 different news articles. These rubrics represent the information that assessors believe is important for readers to assess an article's trustworthiness. The assessors then used their rubrics to manually judge the participating teams' submitted runs. To make these tasks and their rubrics reusable, we have created an automated process to judge runs not part of the original assessing. We show that our AutoJudge ranks existing runs well compared to the TREC human-assessed evaluation (Kendall's τ = 0.678 for Task 1 and τ = 0.872 for Task 2). These resources enable both the evaluation of RAG systems for assistive news trustworthiness assessment and, with the human evaluation as a benchmark, research on improving automated RAG evaluation.

Recent research has shown that neural information retrieval techniques may be susceptible to adversarial attacks. Adversarial attacks seek to manipulate the ranking of documents, with the intention of exposing users to targeted content. In this article, we introduce the Embedding Perturbation Rank Attack ( EMPRA ) method, a novel approach designed to perform adversarial attacks on black-box Neural Ranking Models (NRMs) . EMPRA manipulates sentence-level embeddings, guiding them towards pertinent context related to the query while preserving semantic integrity. This process generates adversarial texts that seamlessly integrate with the original content and remain imperceptible to humans. Our extensive evaluation conducted on the widely used MS MARCO V1 passage collection as well as the TREC DL 2019 and TREC DL 2020 benchmarks, demonstrate the effectiveness of EMPRA against a wide range of state-of-the-art baselines in promoting a specific set of target documents within a given ranked results. Specifically, on MS MARCO Dev set queries, EMPRA successfully achieves a re-ranking of almost 96% of target documents originally ranked between 51 and 100 to rank within the top 10. Furthermore, EMPRA does not rely on surrogate models for generating adversarial documents, enhancing its robustness against various victim NRMs in realistic settings.

Large Language Models (LLMs) are increasingly used to automate relevance judgments for information retrieval (IR) tasks, often demonstrating agreement with human labels that approaches inter-human agreement. To assess the robustness and reliability of LLM-based relevance judgments, we systematically investigate impact of prompt sensitivity on the task. We collected prompts for relevance assessment from 15 human experts and 15 LLMs across three tasks-binary, graded, and pairwise-yielding 90 prompts in total. We compare LLM-generated labels with TREC official human labels using Cohen's κ and pairwise agreement measures. In addition, we compare human- and LLM-generated prompts and analyze differences among different LLMs as judges. We release all data and prompts at https://github.com/Narabzad/prompt-sensitivity-relevance-judgements/.

Large Language Models (LLMs) are increasingly deployed in both academic and industry settings to automate the evaluation of information seeking systems, particularly by generating graded relevance judgments. Several studies report Kendall τ correlations exceeding 0.85 when comparing system rankings derived from human versus LLM-generated relevance labels. Previous work on LLM-based relevance assessment has primarily focused on replicating graded human relevance judgments through various prompting strategies. However, there has been limited exploration of alternative assessment methods or comprehensive comparative studies. In this paper, we systematically compare multiple LLM-based relevance assessment methods, including binary relevance judgments, graded relevance assessments, pairwise preference-based methods, and two nugget-based evaluation methods~-~document-agnostic and document-dependent. Wherever possible, we employ state-of-the-art tools and optimized prompts tailored for these methods. In addition to a traditional comparison based on system rankings using Kendall correlations, we also examine how well LLM judgments align with human preferences, as inferred from relevance grades. We conduct extensive experiments on datasets from three TREC Deep Learning tracks 2019, 2020 and 2021 as well as the ANTIQUE dataset, which focuses on non-factoid open-domain question answering. Beyond dataset-specific results, our work offers a practical methodology for evaluating diverse LLM-based relevance assessment methods. As part of our data release, we include relevance judgments generated by both an open-source (Llama3.2b) and a commercial (gpt-4o) model. Our goal is to reproduce various LLM-based relevance judgment methods to provide a comprehensive comparison. We release all the relevance judgments as a resource that establishes a baseline for future work, ensuring a level playing field for evaluation of LLM-based relevance judgments. All code, data, and resources are publicly available in our GitHub Repository at https://github.com/Narabzad/llm-relevance-judgement-comparison

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. Building on the success of previous workshops, which established foundations in automated judgments and RAG evaluation, this third iteration aims to address emerging challenges as IR systems become increasingly personalized and interactive. The main goal of the third LLM4Eval workshop is to bring together researchers from industry and academia to explore three critical areas: the evaluation of personalized IR systems while maintaining fairness, the boundaries between automated and human assessment in subjective scenarios, and evaluation methodologies for systems that combine multiple IR paradigms (search, recommendations, and dialogue). By examining these challenges, we seek to understand how evaluation approaches can evolve to match the sophistication of modern IR applications. The format of the workshop is interactive, including roundtable discussion sessions, fostering dialogue about the future of IR evaluation while avoiding one-sided discussions. This is the third iteration of the workshop series, following successful events at SIGIR 2024 and WSDM 2025, with the first iteration attracting over 50 participants.

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4Eval models have been studied in the context of automated judgments, natural language generation, and retrieval augmented generation systems. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of LLMs with applications to LLM4Eval tasks. The main goal of LLM4Eval workshop is to bring together researchers from industry and academia to discuss various aspects of LLMs for evaluation in information retrieval, including automated judgments, retrieval-augmented generation pipeline evaluation, altering human evaluation, robustness, and trustworthiness of LLMs for evaluation in addition to their impact on real-world applications. We also plan to run an automated judgment challenge prior to the workshop, where participants will be asked to generate labels for a given dataset while maximising correlation with human judgments. The format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference. This is the second iteration of the workshop. The first version was held in conjunction with SIGIR 2024, attracting over 50 participants.

With the rapid development of Large Language Models (LLMs), recent studies employed LLMs as recommenders to provide personalized information services for distinct users. Despite efforts to improve the accuracy of LLM-based recommendation models, relatively little attention is paid to beyond-utility dimensions. Moreover, there are unique evaluation aspects of LLM-based recommendation models, which have been largely ignored. To bridge this gap, we explore four new evaluation dimensions and propose a multidimensional evaluation framework. The new evaluation dimensions include: 1) history length sensitivity, 2) candidate position bias, 3) generation-involved performance, and 4) hallucinations. All four dimensions have the potential to impact performance, but are largely unnecessary for consideration in traditional systems. Using this multidimensional evaluation framework, along with traditional aspects, we evaluate the performance of seven LLM-based recommenders, with three prompting strategies, comparing them with six traditional models on both ranking and re-ranking tasks on four datasets. We find that LLMs excel at handling tasks with prior knowledge and shorter input histories in the ranking setting, and perform better in the re-ranking setting, beating traditional models across multiple dimensions. However, LLMs exhibit substantial candidate position bias issues, and some models hallucinate nonexistent items much more often than others. We intend our evaluation framework and observations to benefit future research on the use of LLMs as recommenders. The code and data are available at https://github.com/JiangDeccc/EvaLLMasRecommender.

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4Eval models have been studied in the context of automated judgments, natural language generation, and retrieval augmented generation systems. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of LLMs with applications to LLM4Eval tasks. The main goal of LLM4Eval workshop is to bring together researchers from industry and academia to discuss various aspects of LLMs for evaluation in information retrieval, including automated judgments, retrieval-augmented generation pipeline evaluation, altering human evaluation, robustness, and trustworthiness of LLMs for evaluation in addition to their impact on real-world applications. We also plan to run an automated judgment challenge prior to the workshop, where participants will be asked to generate labels for a given dataset while maximising correlation with human judgments. The format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference.

Measurement is a core tool for improving information interaction. What is measured and how it is measured influences how we perceive the efficacy of retrieval and other interactions. While some measures have become ubiquitous, such as precision and recall, there are many other facets of information interaction we either immature, weakly accepted or only used occasionally. This workshop seeks to develop our metrics for information interaction and retrieval, finding novel measures, or innovative adaptations of existing measures, to create a better experimental toolset to improve our understanding of information interaction, and help develop more effective systems and interactions that support it.

Many recent QA models retrieve answers from passages, rather than whole documents, due to the limitations of deep learning models with limited context size. However, this approach ignores important document-level cues that can be crucial in answering questions. This paper reviews three open-domain QA benchmarks from a document-level perspective and finds that they are biased towards passage-level information. Out of 17,000 assessed questions, 82 were identified as requiring document-level reasoning and could not be answered by passage-based models. Document-level retrieval (BM25) outperformed both dense and sparse passage-level retrieval on these questions, highlighting the need for more evaluation of models' ability to understand documents, an often-overlooked challenge in open-domain QA.

Preference judgments have been established as an effective method for offline evaluation of information retrieval systems with advantages to graded or binary relevance judgments. Graded judgments assign each document a pre-defined grade level, while preference judgments involve assessing a pair of items presented side by side and indicating which is better. However, leveraging preference judgments may require a more extensive number of judgments, and there are limitations in terms of evaluation measures. In this study, we present a new preference judgment tool called JUDGO, designed for expert assessors and researchers. The tool is supported by a new heap-like preference judgment algorithm that assumes transitivity and allows for ties. An earlier version of the tool was employed by NIST to determine up to the top-10 best items for each of the 38 topics for the TREC 2022 Health Misinformation track, with over 2,200 judgments collected. The current version has been applied in a separate research study to collect almost 10,000 judgments, with multiple assessors completing each topic. The code and resources are available at https://judgo-system.github.io.

Despite recent progress on conversational systems, they still do not perform smoothly when faced with ambiguous requests. When questions are unclear, conversational systems should have the ability to ask clarifying questions, rather than assuming a particular interpretation or simply responding that they do not understand. While the research community has paid substantial attention to the problem of predicting query ambiguity in traditional search contexts, researchers have paid relatively little attention to predicting when this ambiguity is sufficient to warrant clarification in the context of conversational systems. In this paper, we propose an unsupervised method for predicting the need for clarification. This method is based on the measured coherency of results from an initial answer retrieval step, under the assumption that a less ambiguous query is more likely to retrieve more coherent results when compared to an ambiguous query. We build a graph from retrieved items based on their context similarity, treating measures of graph connectivity as indicators of ambiguity. We evaluate our approach on two open-domain conversational question answering datasets, ClariQ and AmbigNQ, comparing it with neural and non-neural baselines. Our unsupervised approach performs as well as supervised approaches while providing better generalization.

Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations of both the queries and the collection. As a result, retrieval performance is restricted by semantic discrepancies and vocabulary gaps. On the other hand, transformer-based dense retrievers introduce significant improvements in information retrieval tasks by exploiting low-dimensional contextualized representations of the corpus. While dense retrievers are known for their relative effectiveness, they suffer from lower efficiency and lack of generalization issues, when compared to sparse retrievers. For a lightweight retrieval task, high computational resources and time consumption are major barriers encouraging the renunciation of dense models despite potential gains. In this work, we propose boosting the performance of sparse retrievers by expanding both the queries and the documents with linked entities in two formats for the entity names: 1) explicit and 2) hashed. We employ a zero-shot end-to-end dense entity linking system for entity recognition and disambiguation to augment the corpus. By leveraging the advanced entity linking methods, we believe that the effectiveness gap between sparse and dense retrievers can be narrowed. We conduct our experiments on the MS MARCO passage dataset. Since we are concerned with the early stage retrieval in cascaded ranking architectures of large information retrieval systems, we evaluate our results using [email protected] Our approach is also capable of retrieving documents for query subsets judged to be particularly difficult in prior work. We further demonstrate that the non-expanded and the expanded runs with both explicit and hashed entities retrieve complementary results. Consequently, we adopt a run fusion approach to maximize the benefits of entity linking.

The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items in the top ranks, it becomes difficult to recognize meaningful differences between them and to build reusable test collections. Several recent papers explore pairwise preference judgments as an alternative to traditional graded relevance assessments. Rather than viewing items one at a time, assessors view items side-by-side and indicate the one that provides the better response to a query, allowing fine-grained distinctions. If we employ preference judgments to identify the probably best items for each query, we can measure rankers by their ability to place these items as high as possible. We frame the problem of finding best items as a dueling bandits problem. While many papers explore dueling bandits for online ranker evaluation via interleaving, they have not been considered as a framework for offline evaluation via human preference judgments. We review the literature for possible solutions. For human preference judgments, any usable algorithm must tolerate ties, since two items may appear nearly equal to assessors, and it must minimize the number of judgments required for any specific pair, since each such comparison requires an independent assessor. Since the theoretical guarantees provided by most algorithms depend on assumptions that are not satisfied by human preference judgments, we simulate selected algorithms on representative test cases to provide insight into their practical utility. Based on these simulations, one algorithm stands out for its potential. Our simulations suggest modifications to further improve its performance. Using the modified algorithm, we collect over 10,000 preference judgments for pools derived from submissions to the TREC 2021 Deep Learning Track, confirming its suitability. We test the idea of best-item evaluation and suggest ideas for further theoretical and practical progress.

Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical value as a prediction. Although treating this as a regression problem seems straightforward, incorporating various contextual information such as the semantic category information of each Place-of-Interest (POI) is a necessary step, and often the bottleneck, in designing an effective mobility prediction model. As opposed to the typical approach, we treat forecasting as a translation problem and propose a novel forecasting through a language generation pipeline. The paper aims to address the human mobility forecasting problem as a language translation task in a sequence-to-sequence manner. A mobility-to-language template is first introduced to describe the numerical mobility data as natural language sentences. The core intuition of the human mobility forecasting translation task is to convert the input mobility description sentences into a future mobility description from which the prediction target can be obtained. Under this pipeline, a two-branch network, SHIFT (Translating Human Mobility Forecasting), is designed. Specifically, it consists of one main branch for language generation and one auxiliary branch to directly learn mobility patterns. During the training, we develop a momentum mode for better connecting and training the two branches. Extensive experiments on three real-world datasets demonstrate that the proposed SHIFT is effective and presents a new revolutionary approach to forecasting human mobility.

Information retrieval researchers often use eye-tracking to gain insights into searchers' decision making processes. In this paper, we present a visualizing method for summarizing the gaze patterns of multiple searchers on search engine result pages (SERPs). Unlike traditional eye-tracking heat maps, this method includes timing information as part of the visualization, providing additional clarity about searcher fixations as time passes. We demonstrate the visualization technique using eye-tracking data collected as part of a previously published search engine user study and show its value in communicating different patterns of searchers' gaze behavior under different user types and query types. We include a code sample in R to facilitate adoption of the method.

Over the last few years, contextualized pre-trained transformer models such as BERT have provided substantial improvements on information retrieval tasks. Traditional sparse retrieval methods such as BM25 rely on high-dimensional, sparse, bag-of-words query representations to retrieve documents. On the other hand, recent approaches based on pre-trained transformer models such as BERT, fine-tune dense low-dimensional contextualized representations of queries and documents in embedding space. While these dense retrievers enjoy substantial retrieval effectiveness improvements compared to sparse retrievers, they are computationally intensive, requiring substantial GPU resources, and dense retrievers are known to be more expensive from both time and resource perspectives. In addition, sparse retrievers have been shown to retrieve complementary information with respect to dense retrievers, leading to proposals for hybrid retrievers. These hybrid retrievers leverage low-cost, exact-matching based sparse retrievers along with dense retrievers to bridge the semantic gaps between query and documents. In this work, we address this trade-off between the cost and utility of sparse vs dense retrievers by proposing a classifier to select a suitable retrieval strategy (i.e., sparse vs. dense vs. hybrid) for individual queries. Leveraging sparse retrievers for queries which can be answered with sparse retrievers decreases the number of calls to GPUs. Consequently, while utility is maintained, query latency decreases. Although we use less computational resources and spend less time, we still achieve improved performance. Our classifier can select between sparse and dense retrieval strategies based on the query alone. We conduct experiments on the MS MARCO passage dataset demonstrating an improved range of efficiency/effectiveness trade-offs between purely sparse, purely dense or hybrid retrieval strategies, allowing an appropriate strategy to be selected based on a target latency and resource budget.

The offline evaluation of search requires us to define a standard against which we measure the quality of results returned by a ranker. Frequently this standard is defined in absolute terms through relevance grades, but it can also be defined in relative terms through preferences. These preferences might be created through explicit preference judgments, derived from relevance grades, or inferred from clicks and other signals. Preferences from multiple sources might even be combined. In contrast to absolute grades, preferences avoid complex definitions of relevance, indicating only that a ranker should favor one result over another. Despite the simplicity and flexibility of preferences, widespread adoption has been limited by the lack of established evaluation measures. Recent work in this direction has taken two approaches: 1) measures based on weighted counts of agreements and disagreements between a set of preferences and an actual ranking generated by a ranker; and 2) measures that translate preferences into gain values for use with traditional measures, such as nDCG. Both approaches require methods for specifying weights or gains that have little or no theoretical foundation, and the values of these measures have no clear and meaningful interpretation. To address these problems, we propose an evaluation measure that computes the similarity between a directed multigraph of preferences and an actual ranking generated by a ranker. The measure computes an ordering for the vertices of the preference graph that maximizes its similarity to the actual ranking under a rank similarity measure. This maximum similarity becomes the value of the measure. Preference graphs are often acyclic, or nearly so, and to compute the measure we extend an approximate greedy algorithm that is known to produce good results for nearly acyclic graphs. For the rank similarity measure we employ Rank Biased Overlap (RBO) which was explicitly created to match the requirements of search and related applications. We validate the new measure over several collections of preferences explored in recent work.

Assessors make preference judgments faster and more consistently than graded judgments. Preference judgments can also recognize distinctions between items that appear equivalent under graded judgments. Unfortunately, preference judgments can require more than linear effort to fully order a pool of items, and evaluation measures for preference judgments are not as well established as those for graded judgments, such as NDCG. In this article, we explore the assessment process for partial preference judgments, with the aim of identifying and ordering the top items in the pool, rather than fully ordering the entire pool. To measure the performance of a ranker, we compare its output to this preferred ordering by applying a rank similarity measure. We demonstrate the practical feasibility of this approach by crowdsourcing partial preferences for the TREC 2019 Conversational Assistance Track, replacing NDCG with a new measure named compatibility . This new measure has its most striking impact when comparing modern neural rankers, where it is able to recognize significant improvements in quality that would otherwise be missed by NDCG.

Metrics of human behaviour and effort lie at the heart of improving information interaction and retrieval. However, while some measurements have become predominant, such as precision and recall, there are many elements of information interaction where either measures have yet to be created, accepted, or widely used. This workshop seeks to tease out these areas, finding novel measures, or novel uses of existing measures, to create better experimental tools to improve our understanding of information interaction, and help develop better systems to support it.

The majority of US Internet users have searched the internet for health-related information. When people conduct these health searches, searching for information about medical treatments is among the more common reasons. While being a convenient and fast method to collect information, when used by people for health search, search engines can be biased toward results saying treatments are helpful, regardless of the truth. The presence of incorrect information in search results may potentially cause harm, especially if people believe what they read without further research or professional medical advice. In this paper, we aim to better understand the decision making process of determining the efficacy of medical treatments using search result pages. We conducted a think-aloud study in order to gain insights on strategies people use during online search for health related topics. We found that, even when participants are careful and focused on the task, biased search engine results can significantly influence people to make decisions consistent with the bias. The chief reason biased search engines results were able to influence participants is that participants often considered what the majority of the search results stated as part of their decision-making. We also found that participants looked for indications of authoritativeness and quality when evaluating online content. While rank bias and a bias towards wanting treatments to be helpful has been found in prior studies, our participants did not reveal these biases as part of their spoken thoughts. Our results imply that more attention should be paid to search engines' biases given people's bias towards accepting the most common answer in the results as the correct answer. When search results are biased toward incorrect results for health-related searches, dire consequences may be the result.

NDCG and similar measures remain standard for the offline evaluation of search, recommendation, question answering and similar systems. These measures require definitions for two or more relevance levels, which human assessors then apply to judge individual documents. Due to this dependence on a definition of relevance, it can be difficult to extend these measures to account for factors beyond relevance. Rather than propose extensions to these measures, we instead propose a radical simplification to replace them. For each query, we define a set of ideal rankings and compute the maximum rank similarity between members of this set and an actual ranking generated by a system. This maximum similarity to an ideal ranking becomes our effectiveness measure, replacing NDCG and similar measures. We propose rank biased overlap (RBO) to compute this rank similarity, since it was specifically created to address the requirements of rank similarity between search results. As examples, we explore ideal rankings that account for document length, diversity, and correctness.

Tools, computing environments, and datasets form the three critical ingredients for teaching and learning the practical aspects of experimental IR research. Assembling these ingredients can often be challenging, particularly in the context of short courses that cannot afford large startup costs. As an initial attempt to address these issues, we describe materials that we have developed for the "Introduction to IR" session at the ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search (AFIRM 2020), which builds on three components: the open-source Lucene search library, cloud-based notebooks, and the MS MARCO dataset. We offer a self-reflective evaluation of our efforts and hope that our lessons shared can benefit future efforts.

Large scale retrieval systems often employ cascaded ranking architectures, in which an initial set of candidate documents are iteratively refined and re-ranked by increasingly sophisticated and expensive ranking models. In this paper, we propose a unified framework for predicting a range of performance-sensitive parameters based on minimizing end-to-end effectiveness loss. The framework does not require relevance judgments for training, is amenable to predicting a wide range of parameters, allows for fine tuned efficiency-effectiveness trade-offs, and can be easily deployed in large scale search systems with minimal overhead. As a proof of concept, we show that the framework can accurately predict a number of performance parameters on a query-by-query basis, allowing efficient and effective retrieval, while simultaneously minimizing the tail latency of an early-stage candidate generation system. On the 50 million document ClueWeb09B collection, and across 25,000 queries, our hybrid system can achieve superior early-stage efficiency to fixed parameter systems without loss of effectiveness, and allows more finely-grained efficiency-effectiveness trade-offs across the multiple stages of the retrieval system.

Traditionally, recommender systems modelled the physical and cyber contextual influence on people's moving, querying, and browsing behaviors in isolation. Yet, searching, querying, and moving behaviors are intricately linked, especially indoors. Here, we introduce a tripartite location-query-browse graph (LQB) for nuanced contextual recommendations. The LQB graph consists of three kinds of nodes: locations, queries, and Web domains. Directed connections only between heterogeneous nodes represent the contextual influences, while connections of homogeneous nodes are inferred from the contextual influences of the other nodes. This tripartite LQB graph is more reliable than any monopartite or bipartite graph in contextual location, query, and Web content recommendations. We validate this LQB graph in an indoor retail scenario with extensive dataset of three logs collected from over 120,000 anonymized, opt-in users over a 1-year period in a large inner-city mall in Sydney, Australia. We characterize the contextual influences that correspond to the arcs in the LQB graph, and evaluate the usefulness of the LQB graph for location, query, and Web content recommendations. The experimental results show that the LQB graph successfully captures the contextual influence and significantly outperforms the state of the art in these applications.

This paper explores a simple question: How would we provide a high-quality search experience on Mars, where the fundamental physical limit is speed-of-light propagation delays on the order of tens of minutes? On Earth, users are accustomed to nearly instantaneous responses from web services. Is it possible to overcome orders-of-magnitude longer latency to provide a tolerable user experience on Mars? In this paper, we formulate the searching from Mars problem as a tradeoff between "effort" (waiting for responses from Earth) and "data transfer" (pre-fetching or caching data on Mars). The contribution of our work is articulating this design space and presenting two case studies that explore the effectiveness of baseline techniques, using publicly available data from the TREC Total Recall and Sessions Tracks. We intend for this research problem to be aspirational as well as inspirational---even if one is not convinced by the premise of Mars colonization, there are Earth-based scenarios such as searching from rural villages in India that share similar constraints, thus making the problem worthy of exploration and attention from researchers.

We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using similar space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression, we represent the treap topology using different alternative compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. We also show how to extend this representation to support incremental updates over the index. Results show that, under the tf-idf scoring scheme, our index uses about the same space as state-of-the-art compact representations, while performing up to 2--20 times faster on ranked single-word, union, or intersection queries. Under the BM25 scoring scheme, our index may use up to 40% more space than the others and outperforms them less frequently but still reaches improvement factors of 2--20 in the best cases. The index supporting incremental updates poses an overhead of 50%--100% over the static variants in terms of space, construction, and query time.

Recently developed retrieval effectiveness measures have incorporated models of user behavior, but have limited themselves to predicting user performance over a single query and response. Accurate prediction of user performance with search systems must incorporate a means to model how users switch between different information sources. For example, a search session may consist of multiple queries with the user making decisions of when to switch from evaluating the current result list to a new result list produced by a query reformulation. Likewise, users may switch to a result list produced by a query suggestion or other interaction mechanism that produces a new search result list. In this paper, we simulate user behavior and investigate optimal switching behavior for a user who must decide when and if to issue their current query to another search engine. As a first step in understanding the problem space, we restrict our investigation and discussion to two top performing runs submitted to the TREC 2005 Robust track. We find four classes of switching behavior that a user would be faced with in making a decision about whether to switch from one result list to another.

Web search engines have made great progress at answering factoid queries. However, they are not well-tailored for managing more complex questions, especially when they require explanation and/or description. The WebQA workshop series aims at exploring diverse approaches to answering questions on the Web. This year, particular emphasis will be given to Community Question Answering (CQA), where comments by the users engaged in the forum communities can be used to answer new questions. Questions posted on the Web can be short and ambiguous (similarly to Web queries to a search engine). These issues make the WebQA task more challenging than traditional QA, and finding the most effective approaches for it remains an open problem.

We present an assessment platform for gathering online relevance judgments for mobile push notifications that will be deployed in the newly-created TREC 2016 Real-Time Summarization (RTS) track. There is emerging interest in building systems that filter social media streams such as tweets to identify interesting and novel content in real time, putatively for delivery to users' mobile phones. In our evaluation design, all participants subscribe to the Twitter streaming API to identify relevant tweets with respect to a set of interest profiles. As the systems generate results, they are pushed in real time to our evaluation broker via a REST API. The broker then "routes" the tweets to assessors who have installed a custom app on their mobile phones. We detail the design of this platform and discuss a number of challenges that need to be tackled in this type of "Living Labs" setup. It is our goal that such an evaluation design will mitigate any issues that have arisen in traditional batch-style evaluations of this type of task.

Push notifications from social media provide a method to keep up-to-date on topics of personal interest. To be effective, notifications must achieve a balance between pushing too much and pushing too little. Push too little and the user misses important updates; push too much and the user is overwhelmed by unwanted information. Using data from the TREC 2015 Microblog track, we explore simple dynamic emission strategies for microblog push notifications. The key to effective notifications lies in establishing and maintaining appropriate thresholds for pushing updates. We explore and evaluate multiple threshold setting strategies, including purely static thresholds, dynamic thresholds without user feedback, and dynamic thresholds with daily feedback. Our best technique takes advantage of daily feedback in a simple yet effective manner, achieving the best known result reported in the literature to date.

How do we evaluate systems that filter social media streams and send users updates via push notifications on their mobile phones? Such notifications must be relevant, timely, and novel. In this paper, we explore various evaluation metrics for this task, focusing specifically on measuring relevance. We begin with an analysis of metrics deployed at the TREC 2015 Microblog evaluations. A simple change to the metrics, reflecting a different assumption, dramatically alters system rankings. Applying another metric, previously used in the TREC Microblog evaluations, again yields different system rankings. We find little correlation between a number of "reasonable" evaluation metrics, which suggests that system effectiveness depends on how you measure it---an undesirable state in IR evaluation. However, we argue that existing evaluation metrics can be generalized into a framework that uses the same underlying contingency table, but places different weights and penalties. Although we stop short of proposing the "one true metric", this framework can guide the future development of a family of metrics that more accurately models user needs.

Due to lack of mature techniques in privacy-preserving information retrieval (IR), concerns about information privacy and security have become serious obstacles that prevent valuable user data to be used in IR research such as studies on query logs, social media, and medical record retrieval. In SIGIR 2014 and SIGIR 2015, we have run the privacy-preserving IR workshops exploring and understanding the privacy and security risks in information retrieval. This year, we continue the efforts of connecting the two disciplines of IR and privacy/security by organizing this workshop. We target on three themes, differential privacy and IR dataset release, privacy in search and browsing, and privacy in social media. The workshop includes panels with researchers from both fields on these three themes, as well as invite industry speakers for real-world challenges. The goals of this workshop include (1) bringing together the two research fields, and (2) yielding fruitful collaborations.

Adult content is pervasive on the web, has been a driving factor in the adoption of the Internet medium, and is responsible for a significant fraction of traffic and revenues, yet rarely attracts attention in research. The research questions surrounding adult content access behaviors are unique, and interesting and valuable research in this area can be done ethically. WSDM 2016 features a half day workshop on Search and Exploration of X-Rated Information (SEXI) for information access tasks related to adult content. While the scope of the workshop remains broad, special attention is devoted to the privacy and security issues surrounding adult content by inviting keynote speakers with extensive experience on these topics. The recent release of the personal data belonging to customers of the adult dating site Ashley Madison provides a timely context for the focus on privacy and security.

No abstract available.

People track news events according to their interests and available time. For a major event of great personal interest, they might check for updates several times an hour, taking time to keep abreast of all aspects of the evolving event. For minor events of more marginal interest, they might check back once or twice a day for a few minutes to learn about the most significant developments. Systems generating streams of updates about evolving events can improve user performance by appropriately filtering these updates, making it easy for users to track events in a timely manner without undue information overload. Unfortunately, predicting user performance on these systems poses a significant challenge. Standard evaluation methodology, designed for Web search and other adhoc retrieval tasks, adapts poorly to this context. In this paper, we develop a simple model that simulates users checking the system from time to time to read updates. For each simulated user, we generate a trace of their activities alternating between away times and reading times. These traces are then applied to measure system effectiveness. We test our model using data from the TREC 2013 Temporal Summarization Track (TST) comparing it to the effectiveness measures used in that track. The primary TST measure corresponds most closely with a modeled user that checks back once a day on average for an average of one minute. Users checking more frequently for longer times may view the relative performance of participating systems quite differently. In light of this sensitivity to user behavior, we recommend that future experiments be built around clearly stated assumptions regarding user interfaces and access patterns, with effectiveness measures reflecting these assumptions.

This half-day tutorial on IR evaluation combines an introduction to classical IR evaluation methods with material on more recent user-oriented approaches. We primarily focus on off-line evaluation, but some material on on-line evaluation is also covered. The broad goal of the tutorial is to equip researchers with an understanding of modern approaches to IR evaluation, facilitating new research on this topic and improving evaluation methodology for emerging areas.

Creating test collections for modern search tasks is increasingly more challenging due to the growing scale and dynamic nature of content, and need for richer contextualization of the statements of request. To address these issues, the TREC Contextual Suggestion Track explored an open test collection, where participants were allowed to submit any web page as a result for a personalized venue recommendation task. This prompts the question on the reusability of the resulting test collection: How does the open nature affect the pooling process? Can participants reliably evaluate variant runs with the resulting qrels? Can other teams evaluate new runs reliably? In short, does the set of pooled and judged documents effectively produce a post hoc test collection? Our main findings are the following: First, while there is a strongly significant rank correlation, the effect of pooling is notable and results in underestimation of performance, implying the evaluation of non-pooled systems should be done with great care. Second, we extensively analyze impacts of open corpus on the fraction of judged documents, explaining how low recall affects the reusability, and how the personalization and low pooling depth aggravate that problem. Third, we outline a potential solution by deriving a fixed corpus from open web submissions.

We are concerned with the effect of using a surrogate assessor to train a passive (i.e., batch) supervised-learning method to rank documents for subsequent review, where the effectiveness of the ranking will be evaluated using a different assessor deemed to be authoritative. Previous studies suggest that surrogate assessments may be a reasonable proxy for authoritative assessments for this task. Nonetheless, concern persists in some application domains---such as electronic discovery---that errors in surrogate training assessments will be amplified by the learning method, materially degrading performance. We demonstrate, through a re-analysis of data used in previous studies, that, with passive supervised-learning methods, using surrogate assessments for training can substantially impair classifier performance, relative to using the same deemed-authoritative assessor for both training and assessment. In particular, using a single surrogate to replace the authoritative assessor for training often yields a ranking that must be traversed much lower to achieve the same level of recall as the ranking that would have resulted had the authoritative assessor been used for training. We also show that steps can be taken to mitigate, and sometimes overcome, the impact of surrogate assessments for training: relevance assessments may be diversified through the use of multiple surrogates; and, a more liberal view of relevance can be adopted by having the surrogate label borderline documents as relevant. By taking these steps, rankings derived from surrogate assessments can match, and sometimes exceed, the performance of the ranking that would have been achieved, had the authority been used for training. Finally, we show that our results still hold when the role of surrogate and authority are interchanged, indicating that the results may simply reflect differing conceptions of relevance between surrogate and authority, as opposed to the authority having special skill or knowledge lacked by the surrogate.

Rank similarity measures provide a method for quantifying differences between search engine results without the need for relevance judgments. For example, the providers of a search service might use such measures to estimate the impact of a proposed algorithmic change across a large number of queries-perhaps millions-identifying those queries where the impact is greatest. In this paper, we propose and validate a family of rank similarity measures, each derived from an associated effectiveness measure. Each member of the family is based on the maximization of effectiveness difference under this associated measure. Computing this maximized effectiveness difference (MED) requires the solution of an optimization problem that varies in difficulty, depending on the associated measure. We present solutions for several standard effectiveness measures, including nDCG, AP, and ERR. Through an experimental validation, we show that MED reveals meaningful differences between retrieval runs. Mathematically, MED is a metric, regardless of the associated measure. Prior work has established a number of other desiderata for rank similarity in the context of search, and we demonstrate that MED satisfies these requirements. Unlike previous proposals, MED allows us to directly translate assumptions about user behavior from any established effectiveness measure to create a corresponding rank similarity measure. In addition, MED cleanly accommodates partial relevance judgments, and if complete relevance information is available, it reduces to a simple difference between effectiveness values.

Given a current news article, we wish to create a succinct query reflecting its content, which may be used to follow the news story over a period of days, or even weeks. In part, the need for succinct queries is occasioned by limitations of commercial social media search engines, which can perform poorly with longer queries. We start by applying established key phrase extraction methods to the article, creating an initial set of candidate query terms. We then generate a series of probe queries, each a subset of these candidate terms, which we apply to search current social media streams. By analyzing the results of these probes, we rank and trim the candidate set to create a succinct query. We present an experimental study of this method based on a collection of news articles taken from March-April 2014, with the resulting succinct queries used to re-query social media one week later.

An online advertisement’s clickthrough rate provides a fundamental measure of its quality, which is widely used in ad selection strategies. Unfortunately, ads placed in contexts where they are rarely viewed—or where users are unlikely to be interested in commercial results—may receive few clicks regardless of their quality. In this article, we model the variability of a user’s browsing behavior for the purpose of click analysis and prediction in sponsored search. Our model incorporates several important contextual factors that influence ad clickthrough rates, including the user’s query and ad placement on search engine result pages. We formally model these factors with respect to the list of ads displayed on a result page, the probability that the user will initiate browsing of this list, and the persistence of the user in browsing the list. We incorporate these factors into existing click models by augmenting them with appropriate query and location biases. Using expectation maximization, we learn the parameters of these augmented models from click signals recorded in the logs of a commercial search engine. To evaluate the performance of the models and to compare them with state-of-the-art performance, we apply standard evaluation metrics, including log-likelihood and perplexity. Our evaluation results indicate that, through the incorporation of query and location biases, significant improvements can be achieved in predicting browsing and click behavior in sponsored search. In addition, we explore the extent to which these biases actually reflect varying behavioral patterns. Our observations confirm that correlations exist between the biases and user search behavior.

While supervised learning-to-rank algorithms have largely supplanted unsupervised query-document similarity measures for search, the exploration of query-document measures by many researchers over many years produced insights that might be exploited in other domains. For example, the BM25 measure substantially and consistently outperforms cosine across many tested environments, and potentially provides retrieval effectiveness approaching that of the best learning-to-rank methods over equivalent features sets. Other measures based on language modeling and divergence from randomness can outperform BM25 in some circumstances. Despite this evidence, cosine remains the prevalent method for determining inter-document similarity for clustering and other applications. However, recent research demonstrates that BM25 terms weights can significantly improve clustering. In this work, we extend that result, presenting and evaluating novel inter-document similarity measures based on BM25, language modeling, and divergence from randomness. In our first experiment we analyze the accuracy of nearest neighborhoods when using our measures. In our second experiment, we analyze using clustering algorithms in conjunction with our measures. Our novel symmetric BM25 and language modeling similarity measures outperform alternative measures in both experiments. This outcome strongly recommends the adoption of these measures, replacing cosine similarity in future work.

The evaluation of clustering quality has proven to be a difficult task. While it is generally agreed that application specific human assessment can provide a reasonable gold standard for clustering evaluation, the use of human assessors is not practical in many real situations. As a result, machine computable internal clustering quality measures (CQMs) are often used in the evaluation process. However, CQMs have their own drawbacks. Despite their extensive use in clustering research and applications, many CQMs have been shown to lack generality. In this paper we present a new CQM with general applicability. The basis of our CQM is a pattern recognition view of clustering's purpose: the unsupervised prediction of behavior from populations. This purpose translates naturally into our new classifier based CQM which we refer to as in formativeness. We show that in formativeness can satisfy core CQM axioms defined in prior research. Additionally, we provide experimental support, showing that in formativeness can outperform many established CQMs by detecting a larger variety of meaningful structures across a range of synthetic datasets, while at the same time exhibiting good performance on each individual dataset. Our results indicate that in formativeness provides a highly general and effective CQM.

The SIGIR 2013 Workshop on Modeling User Behavior for Information Retrieval Evaluation (MUBE 2013) brings together people to discuss existing and new approaches, ways to collaborate, and other ideas and issues involved in improving information retrieval evaluation through the modeling of user behavior.

We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. Results show that our index uses about 20% less space, and performs queries up to three times faster, than state-of-the-art compact representations.

To construct a diversified search test collection, a set of possible subtopics (or intents) needs to be determined for each topic, in one way or another, and perintent relevance assessments need to be obtained. In the TREC Web Track Diversity Task, subtopics are manually developed at NIST, based on results of automatic click log analysis; in the NTCIR INTENT Task, intents are determined by manually clustering 'subtopics strings' returned by participating systems. In this study, we address the following research question: Does the choice of intents for a test collection affect relative performances of diversified search systems? To this end, we use the TREC 2012 Web Track Diversity Task data and the NTCIR-10 INTENT-2 Task data, which share a set of 50 topics but have different intent sets. Our initial results suggest that the choice of intents may affect relative performances, and that this choice may be far more important than how many intents are selected for each topic

Adult content is pervasive on the Web, has been a driving factor in the adoption of the Internet medium. It is responsible for a significant fraction of traffic and revenues, yet rarely attracts attention in research. We propose that the research questions surrounding adult content access behaviors are unique, and we believe interesting and valuable research in this area can be done ethically. The workshop on Search and Exploration of X-Rated Information (SEXI) addresses these issues for information access tasks related to adult content.

Clickthrough rate provides a fundamental measure of advertising quality, which is widely used in ad selection strategies. However, ads placed in contexts where they are rarely viewed, or where users are unlikely to be interested in commercial results, may receive few clicks regardless of their quality. In this paper, we gain insight into user browsing and click behavior for the purpose of click analysis in sponsored search domain. The list of ads displayed on a page, the user's initial motivation to browse this list, and the persistence of the user are among the contextual factors considered in this paper. We propose a probabilistic model for user's browsing and click behavior using these contextual factors. To evaluate the performance of the model, we compare it with state-of-the-art methods. The experimental results confirm that these contextual factors can better reflect user browsing and click behavior in sponsored search.

Time-biased gain provides a unifying framework for information retrieval evaluation, generalizing many traditional effectiveness measures while accommodating aspects of user behavior not captured by these measures. By using time as a basis for calibration against actual user data, time-biased gain can reflect aspects of the search process that directly impact user experience, including document length, near-duplicate documents, and summaries. Unlike traditional measures, which must be arbitrarily normalized for averaging purposes, time-biased gain is reported in meaningful units, such as the total number of relevant documents seen by the user. In prior work, we proposed and validated a closed-form equation for estimating time-biased gain, explored its properties, and compared it to standard approaches. In this paper, we use stochastic simulation to numerically approximate time-biased gain. Stochastic simulation provides greater flexibility that will allow us, in future work, to easily accommodate different types of user behavior and increase the realism of the effectiveness measure.

We develop and discuss a news comment miner that presents distinct viewpoints on a given theme or event. Given a query, the system uses metasearch techniques to find relevant news articles. Relevant articles are then scraped for both article content and comments. Snippets from the comments are sampled and presented to the user, based on theme popularity and contrastiveness to previously selected snippets. The system design focuses on being quicker and more lightweight than recent topic modelling approaches, while still focusing on selecting orthogonal snippets.

Many current effectiveness measures incorporate simplifying assumptions about user behavior. These assumptions prevent the measures from reflecting aspects of the search process that directly impact the quality of retrieval results as experienced by the user. In particular, these measures implicitly model users as working down a list of retrieval results, spending equal time assessing each document. In reality, even a careful user, intending to identify as much relevant material as possible, must spend longer on some documents than on others. Aspects such as document length, duplicates and summaries all influence the time required. In this paper, we introduce a time-biased gain measure, which explicitly accommodates such aspects of the search process. By conducting an appropriate user study, we calibrate and validate the measure against the TREC 2005 Robust Track test collection. We examine properties of the measure, contrasting it to traditional effectiveness measures, and exploring its extension to other aspects and environments. As its primary benefit, the measure allows us to evaluate system performance in human terms, while maintaining the simplicity and repeatability of system-oriented tests. Overall, we aim to achieve a clearer connection between user-oriented studies and system-oriented tests, allowing us to better transfer insights and outcomes from one to the other.

When an ambiguous query is received, a sensible approach is for the information retrieval (IR) system to diversify the results retrieved for this query, in the hope that at least one of the interpretations of the query intent will satisfy the user. Diversity is an increasingly important topic, of interest to both academic researchers (such as participants in the TREC Web and Blog track diversity tasks, or the NTCIR INTENT task), as well as to search engines professionals. In the 2nd edition of the Diversity in Document Retrieval workshop (DDR 2012), we solicited submissions both on approaches and models for diversity, the evaluation of diverse search results, and on applications of diverse search results. This workshop builds upon a successful 1st edition of DDR which was held at ECIR 2011 in Dublin, Ireland.

Traditional editorial effectiveness measures, such as nDCG, remain standard for Web search evaluation. Unfortunately, these traditional measures can inappropriately reward redundant information and can fail to reflect the broad range of user needs that can underlie a Web query. To address these deficiencies, several researchers have recently proposed effectiveness measures for novelty and diversity. Many of these measures are based on simple cascade models of user behavior, which operate by considering the relationship between successive elements of a result list. The properties of these measures are still poorly understood, and it is not clear from prior research that they work as intended. In this paper we examine the properties and performance of cascade measures with the goal of validating them as tools for measuring effectiveness. We explore their commonalities and differences, placing them in a unified framework; we discuss their theoretical difficulties and limitations, and compare the measures experimentally, contrasting them against traditional measures and against other approaches to measuring novelty. Data collected by the TREC 2009 Web Track is used as the basis for our experimental comparison. Our results indicate that these measures reward systems that achieve an balance between novelty and overall precision in their result lists, as intended. Nonetheless, other measures provide insights not captured by the cascade measures, and we suggest that future evaluation efforts continue to report a variety of measures.

The Maximum Entropy Method provides one technique for validating search engine effectiveness measures. Under this method, the value of an effectiveness measure is used as a constraint to estimate the most likely distribution of relevant documents under a maximum entropy assumption. This inferred distribution may then be compared to the actual distribution to quantify the "informativeness" of the measure. The inferred distribution may also be used to estimate values for other effectiveness measures. Previous work focused on traditional effectiveness measures, such as average precision. In this paper, we extend the Maximum Entropy Method to the newer cascade and intent-aware effectiveness measures by considering the dependency of the documents ranked in a results list. These measures are intended to reflect the novelty and diversity of search results in addition to the traditional relevance. Our results indicate that intent-aware measures based on the cascade model are informative in terms of both inferring actual distribution and predicting the values of other retrieval measures.

We evaluate a framework for BM25F-based XML element retrieval. The framework gathers contextual information associated with each XML element into an associated field, which we call a characteristic field. The contents of the element and the contents of the characteristic field are then treated as distinct fields for BM25F weighting purposes. Evidence supporting this framework is drawn from both our own experiments and experiments reported in related work.

Understanding the intent underlying user's queries may help personalize search results and therefore improve user satisfaction. We develop a methodology for using the content of search engine result pages (SERPs) along with the information obtained from query strings to study characteristics of query intent, with a particular focus on sponsored search. This work represents an initial step towards the development and evaluation of an ontology for commercial search, considering queries that reference specific products, brands and retailers. The characteristics of query categories are studied with respect to aggregated user's clickthrough behavior on advertising links. We present a model for clickthrough behavior that considers the influence of such factors as the location of ads and the rank of ads, along with query category. We evaluate our work using a large corpus of clickthrough data obtained from a major commercial search engine. Our findings suggest that query based features, along with the content of SERPs, are effective in detecting query intent. The clickthrough behavior is found to be consistent with the classification for the general categories of query intent, while for product, brand and retailer categories, all is true to a lesser extent.

Despite the wide applicability of clustering methods, their evaluation remains a problem. In this paper, we present a metric for the evaluation of clustering methods. The data set to be clustered is viewed as a sample from a larger population, with clustering quality measured in terms of our predicted ability to discriminate between members of this population. We measure this property by training a classifier to recognize each cluster and measuring the accuracy of this classifier, normalized by a notion of expected accuracy. To demonstrate the applicability of this metric we apply it to Web queries. We investigated a commercially oriented data set of 1700 queries and a general data set of 4000 queries. Both sets are taken from the logs of a commercial Web search engine. Clustering is based on the contents of search engine result pages generated by executing the queries on the search engine from which they were taken. Multiple clustering algorithms are crossed with various weighting schemes to produce multiple clusterings of each query set. Our metric is used evaluate these clusterings. The results on the commercially oriented data set are compared to two pre-existing manual labelings, and are also used in an ad clickthrough experiment.

In this work, we investigate the contribution of query terms and their corresponding ad click rates on commercial intent of queries. A probabilistic model is proposed following the hypothesis that a query is likely to receive ad clicks based on contributions from its individual terms.

Reciprocal Rank Fusion (RRF), a simple method for combining the document rankings from multiple IR systems, consistently yields better results than any individual system, and better results than the standard method Condorcet Fuse. This result is demonstrated by using RRF to combine the results of several TREC experiments, and to build a meta-learner that ranks the LETOR 3 dataset better than any previously reported method

We apply the Dynamic Markov Compression model to detect spam edits in the Wikipedia. The method appears to outperform previous efforts based on compression models, providing performance comparable to methods based on manually constructed rules.

Clickthrough rate, bid, and cost-per-click are known to be among the factors that impact the rank of an ad shown on a search result page. Search engines can benefit from estimating ad clickthrough in order to determine the quality of ads and maximize their revenue. In this paper, a methodology is developed to estimate ad clickthrough rate by exploring user queries and clickthrough logs. As we demonstrate, the average ad clickthrough rate depends to a substantial extent on the rank position of ads and on the total number of ads displayed on the page. This observation is utilized by a baseline model to calculate the expected clickthrough rate for various ads. We further study the impact of query intent on the clickthrough rate, where query intent is predicted using a combination of query features and the content of search engine result pages. The baseline model and the query intent model are compared for the purpose of calculating the expected ad clickthrough rate. Our findings suggest that such factors as the rank of an ad, the number of ads displayed on the result page, and query intent are effective in estimating ad clickthrough rate.

We present a method for modeling, and automaticallyinferring, the current interest of a user in searchadvertising. Our task is complementary to that of predictingad relevance or commercial intent of a query in the aggregate, since the user intent may vary significantly for the same query. To achieve this goal, we develop a fine-grained user interaction model for inferring searcher receptiveness to advertising. We show that modeling the search context and behavior can significantly improve the accuracy of ad clickthrough prediction for the current user, compared to the existing state-of-the-artclassification methods that do not model this additional session level contextual and interaction information. In particular, our experiments over thousands of search sessions from hundreds of real users demonstrate that our model is more effective at predicting ad clickthrough within the same search session. Our work has other potential applications, such as improving searchinterface design (e.g., varying the number or type of ads) based on user interest, and behavioral targeting (e.g., identifying users interested in immediate purchase).

Evaluation measures act as objective functions to be optimized by information retrieval systems. Such objective functions must accurately reflect user requirements, particularly when tuning IR systems and learning ranking functions. Ambiguity in queries and redundancy in retrieved documents are poorly reflected by current evaluation measures. In this paper, we present a framework for evaluation that systematically rewards novelty and diversity. We develop this framework into a specific evaluation measure, based on cumulative gain. We demonstrate the feasibility of our approach using a test collection based on the TREC question answering track.

Index compression techniques are known to substantially decrease the storage requirements of a text retrieval system. As a side-effect, they may increase its retrieval performance by reducing disk I/O overhead. Despite this advantage, developers sometimes choose to store index data in uncompressed form, in order to not obstruct random access into each index term's postings list.

Information retrieval evaluation based on the pooling method is inherently biased against systems that did not contribute to the pool of judged documents. This may distort the results obtained about the relative quality of the systems evaluated and thus lead to incorrect conclusions about the performance of a particular ranking technique.

Web search engines present lists of captions, comprising title, snippet, and URL, to help users decide which search results to visit. Understanding the influence of features of these captions on Web search behavior may help validate algorithms and guidelines for their improved generation. In this paper we develop a methodology to use clickthrough logs from a commercial search engine to study user behavior when interacting with search result captions. The findings of our study suggest that relatively simple caption features such as the presence of all terms query terms, the readability of the snippet, and the length of the URL shown in the caption, can significantly influence users' Web search behavior.

Query logs and pseudo-relevance feedback (PRF) offer ways in which terms to refine Web searchers ’ queries can be selected, offered to searchers, and used to improve search effectiveness. In this poster we present a study of these techniques that aims to characterize the degree of similarity between them across a set of test queries, and the same set broken out by query type. The results suggest that: (i) similarity increases with the amount of evidence provided to the PRF algorithm, (ii) similarity is higher when titles/snippets are used for PRF than full-text, and (iii) similarity is higher for navigational than informational queries. The findings have implications for the combined usage of query logs and PRF in generating query refinement alternatives.

For enterprise search, there exists a relationship between work task and document type that can be used to refine search results. In this poster, we adapt the popular Okapi BM25 scoring function to weight term frequency based on the relevance of a document type to a work task. Also, we use click frequency for each task-type pair to estimate a realistic weight. Using the W3C collection from the TREC Enterprise track for evaluations, our approach leads to significant improvements on search precision.

Professionals in the workplace need high-precision search tools capable of retrieving information that is useful and appropriate to the task at hand. One approach to identifying content, which is not only relevant but also useful, is to make use of the task context of the search. We present X-Site, an enterprise search engine for the software engineering domain that exploits relationships between user's tasks and document genres in the collection to improve retrieval precision.

We present a static index pruning method, to be used in ad-hoc document retrieval tasks, that follows a document-centric approach to decide whether a posting for a given term should remain in the index or not. The decision is made based on the term's contribution to the document's Kullback-Leibler divergence from the text collection's global language model. Our technique can be used to decrease the size of the index by over 90%, at only a minor decrease in retrieval effectiveness. It thus allows us to make the index small enough to fit entirely into the main memory of a single PC, even for large text collections containing millions of documents. This results in great efficiency gains, superior to those of earlier pruning methods, and an average response time around 20 ms on the GOV2 document collection.

We present a new family of hybrid index maintenance strategies to be used in on-line index construction for monotonically growing text collections. These new strategies improve upon recent results for hybrid index maintenance in dynamic text retrieval systems. Like previous techniques, our new method distinguishes between short and long posting lists: While short lists are maintained using a merge strategy, long lists are kept separate and are updated in-place. This way, costly relocations of long posting lists are avoided.We discuss the shortcomings of previous hybrid methods and give an experimental evaluation of the new technique, showing that its index maintenance performance is superior to that of the earlier methods, especially when the amount of main memory available to the indexing system is small. We also present a complexity analysis which proves that, under a Zipfian term distribution, the asymptotical number of disk accesses performed by the best hybrid maintenance strategy is linear in the size of the text collection, implying the asymptotical optimality of the proposed strategy.

We propose an integration of term proximity scoring into Okapi BM25. The relative retrieval effectiveness of our retrieval method, compared to pure BM25, varies from collection to collection.We present an experimental evaluation of our method and show that the gains achieved over BM25 as the size of the underlying text collection increases. We also show that for stemmed queries the impact of term proximity scoring is larger than for unstemmed queries.

We examine issues in the design of fully dynamic information retrieval systems supporting both document insertions and deletions. The two main components of such a system, index maintenance and query processing, affect each other, as high query performance is usually paid for by additional work during update operations. Two aspects of the system -- incremental updates and garbage collection for delayed document deletions -- are discussed, with a focus on the respective indexing vs. query performance trade-offs. Depending on the relative number of queries and update operations, different strategies lead to optimal overall performance.

The direct application of standard ranking techniques to retrieve individual elements from a collection of XML documents often produces a result set in which the top ranks are dominated by a large number of elements taken from a small number of highly relevant documents. This paper presents and evaluates an algorithm that re-ranks this result set, with the aim of minimizing redundant content while preserving the benefits of element retrieval, including the benefit of identifying topic-focused components contained within relevant documents. The test collection developed by the INitiative for the Evaluation of XML Retrieval (INEX) forms the basis for the evaluation.

Context influences the search process, but to date research has not definitively identified which aspects of context are the most influential for information retrieval, and thus are worthy of integration in today's retrieval systems. In this research, we isolated for examination two aspects of context: task and document genre and examined the relationship between them within a software engineering work domain. In this domain, the nature of the task has an impact on decisions of relevance and usefulness, and the document collection contains a distinctive set of genre. Our data set was a document repository created and used by our target population. The document surrogates were meta-tagged by purpose and document type. Correspondence analysis of this categorical data identified some specific relationships between genres and tasks, as well as four broad dimensions of variability underlying these relationships. These results have the potential to inform the design of a contextual retrieval system by refining search results for this domain.

We examine the problem of retrieving the top-m ranked items from a large collection, randomly distributed across an n-node system. In order to retrieve the top m overall, we must retrieve the top m from the subcollection stored on each node and merge the results. However, if we are willing to accept a small probability that one or more of the top-m items may be missed, it is possible to reduce computation time by retrieving only the top k < m from each node. In this paper, we demonstrate that this simple observation can be exploited in a realistic application to produce a substantial efficiency improvement without compromising the quality of the retrieved results. To support our claim, we present a statistical model that predicts the impact of the optimization. The paper is structured around a specific application~---~passage retrieval for question answering~---~but the primary results are more broadly applicable.

Experiments were conducted to explore the impact of combining various components of eight leading information retrieval systems. Each system demonstrated improved effectiveness with the use of blind feedback, in which the results of a preliminary retrieval step were used to augment the efficacy of a secondary retrieval step. The hybrid combination of primary and secondary retrieval steps from different systems in a number of cases yielded better effectiveness than either of the constituent systems alone. This positive combining effect was observed when entire documents were passed between the two retrieval steps, but not when only the expansion terms were passed. Several combinations of primary and secondary retrieval steps were fused using the CombMNZ algorithm; all yielded significant effectiveness improvement over the individual systems, with the best yielding a an improvement of 13% (p = 10-6) over the best individual system and an improvement of 4% (p = 10-5) over a simple fusion of the eight systems.

An usual approach to address mismatching vocabulary problem is to augment the original query using dictionaries and other lexical resources and/or by looking at pseudo-relevant documents. Either way, terms are added to form a new query that will be used to score all documents in a subsequent retrieval pass, and as consequence the original query's focus may drift because of the newly added terms. We propose a new method to address the mismatching vocabulary problem, expanding original query terms only when necessary and complementing the user query for missing terms while scoring documents. It allows related semantic aspects to be included in a conservative and selective way, thus reducing the possibility of query drift. Our results using replacements for the missing query terms in modified document and passages retrieval methods show significant improvement over the original ones.

INTRODUCTION A widely-used architecture for factoid question answering (QA) involves the use of a multi-step pipeline consisting of: 1) initial question analysis, 2) document and/or passage retrieval, and 3) answer extraction. In this study, we examine the relationship between the quality of document retrieval and the overall accuracy of QA systems. We evaluate two QA systems using TREC 2002 test set questions [9]: Carnegie Mellon's JAVELIN system [7] and Waterloo's MultiText QA system [2]. We adapt the two QA systems in order to use di#erent sets of documents as input, and seven different document retrieval methods to create the list of documents including a combination of di#erent systems. The set of known relevant documents was used as a baseline to compare the di#erent retrieval methods. Documents with exact or inexact judgments are considered relevant. Our main hypothesis for this study is that there is a positive relationship between improved document retrieval and QA accuracy

We present a storage management layer that facilitates the implementation of parallel information retrieval systems, and related applications, on networks of workstations. The storage management layer automates the process of adding and removing nodes, and implements a dispersed mirroring strategy to improve reliability. When nodes are added and removed, the document collection managed by the system is redistributed for load balancing purposes. The use of dispersed mirroring minimizes the impact of node failures and system modifications on query performance.

No abstract available.

A major concern in the implementation of a distributed Web crawler is the choice of a strategy for partitioning the Web among the nodes in the system. Our goal in selecting this strategy is to minimize the overlap between the activities of individual nodes. We propose a topic-oriented approach, in which the Web is partitioned into general subject areas with a crawler assigned to each. We examine design alternatives for a topic-oriented distributed crawler, including the creation of a Web page classifier for use in this context. The approach is compared experimentally with a hash-based partitioning, in which crawler assignments are determined by hash functions computed over URLs and page contents. The experimental evaluation demonstrates the feasibility of the approach, addressing issues of communication overhead, duplicate content detection, and page quality assessment.

Using our question answering system, questions from the TREC 2001 evaluation were executed over a series of Web data collections, with the sizes of the collections increasing from 25 gigabytes up to nearly a terabyte.

Our goal is to automatically answer brief factual questions of the form ``When was the Battle of Hastings?'' or ``Who wrote The Wind in the Willows?''. Since the answer to nearly any such question can now be found somewhere on the Web, the problem reduces to finding potential answers in large volumes of data and validating their accuracy. We apply a method for arbitrary passage retrieval to the first half of the problem and demonstrate that answer redundancy can be used to address the second half. The success of our approach depends on the idea that the volume of available Web data is large enough to supply the answer to most factual questions multiple times and in multiple contexts. A query is generated from a question and this query is used to select short passages that may contain the answer from a large collection of Web data. These passages are analyzed to identify candidate answers. The frequency of these candidates within the passages is used to ``vote'' for the most likely answer. The approach is experimentally tested on questions taken from the TREC-9 question-answering test collection. As an additional demonstration, the approach is extended to answer multiple choice trivia questions of the form typically asked in trivia quizzes and television game shows.

We present a model for arbitrary passage retrieval using Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the order of their expected relevance. Features such as phrase matching, truncation, and stemming integrate naturally into the model. Properties of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is comparable to that of standard ranking techniques. Since global statistics are not used, the method is of particular value in distributed environments. Since ranking is based on arbitrary passages, the structural elements to be ranked may be specified at query time and do not need to be restricted to predefined elements.

No abstract available.

No abstract available.

Test collections with a million or more documents are needed for the evaluation of modern information retrieval systems.Yet their construction requires a great deal of effort.Judgements must be rendered as to whether or not documents are relevant to each of a set of queries.Exhaustive judging, in which every document is examined and a judgement rendered, is infeasible for collections of this size.Current practice is represented by the "pooling method", as used in the TREC conference series, in which only the first k documents from each of a number of sources are judged.We propose two methods, Intemctive Searching and Judging and Moveto-front Pooling, that yield effective test collections while requiring many fewer judgements.Interactive Searching and Judging selects documents to be judged using an interactive search system, and may be used by a small research team to develop an effective test collection using minimal resources.Move-to-Front Pooling directly improves on the standard pooling method by using a variable number of documents from each source depending on its retrieval performance.Move-to-Front Pooling would be an appropriate replacement for the standard pooling method in future collection development efforts involving many independent groups.