Abstract How to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments.

The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and suffer from complex search scenarios where user interactions are absent or biased. Recently, the advances in portable and high-precision brain-computer interface (BCI) devices have shown the possibility to monitor user’s brain activities during search process. Brain signals can directly reflect user’s psychological responses to search results and thus it can act as additional and unbiased RF signals. To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based RF with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking. The experimental results on the user study dataset show that incorporating brain signals leads to significant performance improvement in our RF framework. Besides, we observe that brain signals perform particularly well in several hard search scenarios, especially when implicit signals as feedback are missing or noisy. This reveals when and how to exploit brain signals in the context of RF.

Short video~(SV) online streaming has been one of the most popular Internet applications in recent years. When browsing SVs, users gradually immerse themselves and derive relaxation or knowledge. Whereas prolonged browsing will lead to a decline in positive feelings, users continue due to inertia, resulting in decreased satisfaction. Immersion is shown to be an essential factor for users' positive experience and highly related to users' interactions in film, games, and virtual reality. However, immersion in SV interaction is still unexplored, which differs from the previously studied scenarios essentially because SV delivery is fragmented, discrete, and with limited time for each video.

Passage ranking involves two stages: passage retrieval and passage re-ranking, which are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues. To address this problem, we introduce T2Ranking, a large-scale Chinese benchmark for passage ranking. T2Ranking comprises more than 300K queries and over 2M unique passages from real-world search engines. Expert annotators are recruited to provide 4-level graded relevance scores (fine-grained) for query-passage pairs instead of binary relevance judgments (coarse-grained). To ease the false negative issues, more passages with higher diversities are considered when performing relevance annotations, especially in the test set, to ensure a more accurate evaluation. Apart from the textual query and passage data, other auxiliary resources are also provided, such as query types and XML files of documents which passages are generated from, to facilitate further studies. To evaluate the dataset, commonly used ranking models are implemented and tested on T2Ranking as baselines. The experimental results show that T2Ranking is challenging and there is still scope for improvement. The full data and all codes are available at https://github.com/THUIR/T2Ranking/.

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we still know little about how they perform in terms of interpolation and extrapolation. In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. Firstly, we examine existing ad-hoc search benchmarks from the two perspectives. We investigate the distribution of training and test data and find a considerable overlap in query entities, query intent, and relevance labels. This finding implies that the evaluation on these test sets is biased toward interpolation and cannot accurately reflect the extrapolation capacity. Secondly, we propose a novel evaluation protocol to separately evaluate the interpolation and extrapolation performance on existing benchmark datasets. It resamples the training and test data based on query similarity and utilizes the resampled dataset for training and evaluation. Finally, we leverage the proposed evaluation protocol to comprehensively revisit a number of widely-adopted neural retrieval models. Results show models perform differently when moving from interpolation to extrapolation. For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation. Therefore, it is necessary to separately evaluate both interpolation and extrapolation performance and the proposed resampling method serves as a simple yet effective evaluation tool for future IR studies.

Recently, pre-training methods tailored for IR tasks have achieved great success. However, as the mechanisms behind the performance improvement remain under-investigated, the interpretability and robustness of these pre-trained models still need to be improved. Axiomatic IR aims to identify a set of desirable properties expressed mathematically as formal constraints to guide the design of ranking models. Existing studies have already shown that considering certain axioms may help improve the effectiveness and interpretability of IR models. However, there still lack efforts of incorporating these IR axioms into pre-training methodologies. To shed light on this research question, we propose a novel pre-training method with \underlineA xiomatic \underlineRe gularization for ad hoc \underlineS earch (ARES). In the ARES framework, a number of existing IR axioms are re-organized to generate training samples to be fitted in the pre-training process. These training samples then guide neural rankers to learn the desirable ranking properties. Compared to existing pre-training approaches, ARES is more intuitive and explainable. Experimental results on multiple publicly available benchmark datasets have shown the effectiveness of ARES in both full-resource and low-resource (e.g., zero-shot and few-shot) settings. An intuitive case study also indicates that ARES has learned useful knowledge that existing pre-trained models (e.g., BERT and PROP) fail to possess. This work provides insights into improving the interpretability of pre-trained models and the guidance of incorporating IR axioms or human heuristics into pre-training methods.

Web search heavily relies on click-through behavior as an essential feedback signal for performance evaluation and improvement. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click is regarded as a signal of irrelevance or uselessness. However, there are many cases where users satisfy their information need with the contents shown on the Search Engine Result Page (SERP). This raises the problem of measuring the usefulness of non-click results and modeling user satisfaction in such circumstances.

While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades. With the maturity of the Brain-Machine Interface(BMI), we build an efficient and effective communication system between human beings and search engines based on electroencephalogram (EEG) signals, called Brain Machine Search Interface (BMSI)system. The BMSI system provides functions including query reformulation and search result interaction. In our system, users can perform search tasks without having to use the mouse and keyboard. Therefore, it is useful for application scenarios in which hand-based interactions are infeasible, e.g, for users with severe neuromuscular disorders. Besides, based on brain signals decoding, our system can provide abundant and valuable user-side context information (e.g., real-time satisfaction feedback, extensive context information, and a clearer description of information needs) to the search engine, which is hard to capture in the previous paradigm. In our implementation, the system can decode user satisfaction from brain signals in real-time during the interaction process and re-rank the search results list based on user satisfaction feedback.The demo video is available at http://www.thuir.cn/group/YQLiu/videos/BMSISystem.html

Reading comprehension is a complex cognitive process involving many human brain activities. However, little is known about what happens in human brain during reading comprehension and how these cognitive activities can affect information retrieval process. Additionally, with the advances in brain imaging techniques such as electroencephalogram (EEG), it is possible to collect brain signals in almost real time and explore whether it can be utilized as feedback to facilitate information acquisition performance.

Click models are widely used for user simulation, relevance inference, and evaluation in Web search. Most existing click models implicitly assume that users’ relevance judgment and behavior patterns are homogeneous. However, previous studies have shown that different users interact with search engines in rather different ways. Therefore, a unified click model can hardly capture the heterogeneity in users’ click behavior. To shed light on this research question, we propose a Click Model Personalization framework (CMP) that adaptively selects from global and local models for individual users. Different adaptive strategies are designed to personalize click behavior modeling only for specific users and queries. We also reveal that capturing personalized behavior patterns is more important than modeling personalized relevance assessments in constructing personalized click models. To evaluate the performance of the proposed CMP framework, we build a large-scale practical Personalized Web Search (PWS) dataset, which consists of the search logs of 1,249 users from a commercial search engine over six months. Experimental results show that the proposed CMP framework achieves significant performance improvements than the non-personalized click models in click prediction.

Parkinson's Disease (PD) is a commonly diagnosed movement disorder with more than 10 million patients worldwide. Its clinical evaluation relies on a rating system called MDS-UPDRS, which includes subjective and error-prone motor examinations. This paper proposes an objective and interpretable visual system (PD-Net ) to quantitatively evaluate motor function of PD patients using video footage. The PD-Net consists of three modules: 1) a pose detector to infer 21 hand keypoints directly from RGB videos, 2) a movement analysis module to study temporal patterns of hand keypoints and discover motor symptoms, and 3) a scoring module to predict MDS-UPDRS ratings with retrieved symptoms. Trained with an in-house clinical dataset, PD-Net can effectively handle the unique challenges of PD examination videos, such as clinically-defined gestures, distinct self-occlusion/foreshortening effect and contextual background. And it detects hand keypoints of PD patients with an average accuracy of 84.1%, a 32.9% improvement over OpenPose. When compared to the ratings of experienced clinicians, PD-Net achieves an overall MDS-UPDRS rating score accuracy of 87.6% and Cohen's kappa of 0.82 on a testing dataset of 509 examination videos at a level exceeding human raters. This study demonstrates a clinically applicable automated video analysis system for PD clinical evaluation, which can facilitate early detection, routine monitoring, and treatment assessment.

Extracting valuable feedback information from user behavior logs is one of the major concerns in Web search studies. Among the tremendous efforts that aim to improve search performance with user behavior modeling, constructing click models is of vital importance because it provides a direct estimation of result relevance. Most existing click models assume that whether or not users click on results only depends on the examination probability and the content of the result. However, through a carefully designed user eye-tracking study, we found that users do not make click-through decisions in isolation. Instead, they also consider the context of a result (e.g., adjacent results). This finding leads to the design of a novel click model named Comparison-based Click Model (CBCM). Different from traditional examination hypotheses, CBCM introduces the concept of an examination viewport and assumes users click results after comparing adjacent results within the same viewport. The experimental results on a publicly available user behavior dataset demonstrate the effectiveness of CBCM. We also public our code of CBCM and dataset.

Search applications such as image search, app search and product search are crucial parts of web search, which we denote as vertical search services. This tutorial will introduce the research and applications of user behavior modeling for vertical search. The bulk of the tutorial is devoted to covering research into behavior patterns, user behavior models and applications of user behavior data to refine evaluation metrics and ranking models for web-based vertical search.

Following the success of Cranfield-like evaluation approaches to evaluation in web search, web image search has also been evaluated with absolute judgments of (graded) relevance. However, recent research has found that collecting absolute relevance judgments may be difficult in image search scenarios due to the multi-dimensional nature of relevance for image results. Moreover, existing evaluation metrics based on absolute relevance judgments do not correlate well with search users' satisfaction perceptions in web image search.

Evaluation metrics play an important role in the batch evaluation of IR systems. Based on a user model that describes how users interact with the rank list, an evaluation metric is defined to link the relevance scores of a list of documents to an estimation of system effectiveness and user satisfaction. Therefore, the validity of an evaluation metric has two facets: whether the underlying user model can accurately predict user behavior and whether the evaluation metric correlates well with user satisfaction. While a tremendous amount of work has been undertaken to design, evaluate, and compare different evaluation metrics, few studies have explored the consistency between these two facets of evaluation metrics. Specifically, we want to investigate whether the metrics that are well calibrated with user behavior data can perform as well in estimating user satisfaction. To shed light on this research question, we compare the performance of various metrics with the C/W/L Framework in estimating user satisfaction when they are optimized to fit observed user behavior. Experimental results on both self-collected and public available user search behavior datasets show that the metrics optimized to fit users' click behavior can perform as well as those calibrated with user satisfaction feedback. We also investigate the reliability in the calibration process of evaluation metrics to find out how much data is required for parameter tuning. Our findings provide empirical support for the consistency between user behavior modeling and satisfaction measurement, as well as guidance for tuning the parameters in evaluation metrics.

In web image search, items users search for are images instead of Web pages or online services. Web image search constitutes a very important part of web search. Re-ranking is a trusted technique to improve retrieval effectiveness in web search. Previous work on re-ranking web image search results mainly focuses on intra-query information (e.g., human interactions with the initial list of the current query). Contextual information such as the query sequence and implicit user feedback provided during a search session prior to the current query is known to improve the performance of general web search but has so far not been used in web image search. The differences in result placement and interaction mechanisms of image search make the search process rather different from general Web search engines. Because of these differences, context-aware re-ranking models that have originally been developed for general web search cannot simply be applied to web image search. We propose CARM, a context-aware re-ranking model, a neural network-based framework to re-rank web image search results for a query based on previous interaction behavior in the search session in which the query was submitted. Specifically, we explore a hybrid encoder with an attention mechanism to model intra-query and inter-query user preferences for image results in a two-stage structure. We train context-aware re-ranking model (CARM) to jointly learn query and image representations so as to be able to deal with the multimodal characteristics of web image search. Extensive experiments are carried out on a commercial web image search dataset. The results show that CARM outperforms state-of-the-art baseline models in terms of personalized evaluation metrics. Also, CARM combines the original ranking can improve the original ranking on personalized ranking and relevance estimation. We make the implementation of CARM and relevant datasets publicly available to facilitate future studies.

Web-based image search engines differ from Web search engines greatly. The intents or goals behind human interactions with image search engines are different. In image search, users mainly search images instead of Web pages or online services. It is essential to know why people search for images because user satisfaction may vary as intent varies. Furthermore, image search engines show results differently. For example, grid-based placement is used in image search instead of the linear result list, so that users can browse result list both vertically and horizontally. Different user intents and system UIs lead to different user behavior. Thus, it is hard to apply standard user behavior models developed for general Web search to image search. To better understand user intent and behavior in image search scenarios, we plan to conduct the lab-based user study, field study and commercial search log analysis. We then propose user behavior models based on the observation from data analysis to improve the performance of Web image search engines.

Compared to general web search engines, web image search engines display results in a different way. In web image search, results are typically placed in a grid-based manner rather than a sequential result list. In this scenario, users can view results not only in a vertical direction but also in a horizontal direction. Moreover, pagination is usually not (explicitly) supported on image search search engine result pages (SERPs), and users can view results by scrolling down without having to click a “next page” button. These differences lead to different interaction mechanisms and user behavior patterns, which, in turn, create challenges to evaluation metrics that have originally been developed for general web search. While considerable effort has been invested in developing evaluation metrics for general web search, there has been relatively little effort to construct grid-based evaluation metrics.

User interaction behavior is a valuable source of implicit relevance feedback. In Web image search a different type of search result presentation is used than in general Web search, which leads to different interaction mechanisms and user behavior. For example, image search results are self-contained, so that users do not need to click the results to view the landing page as in general Web search, which generates sparse click data. Also, two-dimensional result placement instead of a linear result list makes browsing behaviors more complex. Thus, it is hard to apply standard user behavior models (e.g., click models) developed for general Web search to Web image search. In this paper, we conduct a comprehensive image search user behavior analysis using data from a lab-based user study as well as data from a commercial search log. We then propose a novel interaction behavior model, called grid-based user browsing model (GUBM), whose design is motivated by observations from our data analysis. GUBM can both capture users' interaction behavior, including cursor hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1) It is based on an unsupervised learning method and does not need manually annotated data for training. (2) It is based on user interaction features on search engine result pages (SERPs) and is easily transferable to other scenarios that have a grid-based interface such as video search engines. We conduct extensive experiments to test the performance of our model using a large-scale commercial image search log. Experimental results show that in terms of behavior prediction (perplexity), and topical relevance and image quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms state-of-the-art baseline models as well as the original ranking. We make the implementation of GUBM and related datasets publicly available for future studies.

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users» search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling.

Image search engines show results differently from general Web search engines in three key ways: (1) most Web-based image search engines adopt the two-dimensional result placement instead of the linear result list; (2) image searches show snapshots instead of snippets (query-dependent abstracts of landing pages) on search engine result pages (SERPs); and (3) pagination is usually not (explicitly) supported on image search SERPs, and users can view results without having to click on the "next page'' button. Compared with the extensive study of user behavior in general Web search scenarios, there exists no thorough investigation how the different interaction mechanism of image search engines affects users' examination behavior. To shed light on this research question, we conducted an eye-tracking study to investigate users' examination behavior in image searches. We focus on the impacts of factors in examination including position, visual saliency, edge density, the existence of textual information, and human faces in result images. Three interesting findings indicate users' behavior biases: (1) instead of the traditional "Golden Triangle'' phenomena in the user examination patterns of general Web search, we observe a middle-position bias, (2) besides the position factor, the content of image results (e.g., visual saliency) affects examination behavior, and (3) some popular behavior assumptions in general Web search (e.g., examination hypothesis) do not hold in image search scenarios. We predict users' examination behavior with different impact factors. Results show that combining position and visual content features can improve prediction in image searches.

Click-through information is considered as a valuable source of users’ implicit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong influence on users’ examination behavior, most existing click models are position based, assuming that users examine results from top to bottom in a linear fashion. Although these click models have been successful, most do not take temporal information into account. As many existing studies have shown, click dwell time and click sequence information are strongly correlated with users’ perceived relevance and search satisfaction. Incorporating temporal information may be important to improve performance of user click models for Web searches. In this article, we investigate the problem of properly incorporating temporal information into click models. We first carry out a laboratory eye-tracking study to analyze users’ examination behavior in different click sequences and find that the user common examination path among adjacent clicks is linear. Next, we analyze the user dwell time distribution in different search logs and find that we cannot simply use a click dwell time threshold (e.g., 30 seconds) to distinguish relevant/irrelevant results. Finally, we propose a novel time-aware click model (TACM), which captures the temporal information of user behavior. We compare the TACM to several existing click models using two real-world search engine logs. Experimental results show that the TACM outperforms other click models in terms of both predicting click behavior (perplexity) and estimating result relevance (NDCG).

Recent advances in DNA sequencing have enabled a flood of sequencing-based applications for studying biology and medicine. A key requirement of these applications is to rapidly and accurately map DNA subsequences to a reference genome. This DNA subsequence mapping problem shares core technical challenges with the similarity query processing problem studied in the database research literature. To solve this problem, existing techniques first extract signatures from a query, then retrieve candidate mapping positions from an index using the extracted signatures, and finally verify the candidate positions. The efficiency of these techniques depends critically on signatures selected from queries, while signature selection relies on an indexing scheme of a reference genome. The q-gram inverted indexing, one of the most widely used indexing schemes, can discover candidate positions quickly, but has the limitation that signatures of queries are restricted to fixed-length q-grams. To address the problem, we propose a flexible way to generate variable-length signatures using a fixed-length q-gram index. The proposed technique groups a few q-grams into a variable-length signature, and generates candidate positions for the variable-length signature using the inverted lists of the q-grams. We also propose a novel dynamic programming algorithm to balance between the filtering power of signatures and the overhead of generating candidate positions for the signatures. Through extensive experiments on both simulated and real genomic data, we show that our technique substantially improves the performance of read mapping in terms of both mapping speed and accuracy.

The explosive growth in the amount of data produced by next-generation sequencing poses significant computational challenges on how to store, transmit and query these data, efficiently and accurately. A unique characteristic of the genomic sequence data is that many of them can be highly similar to each other, which has motivated the idea of compressing sequence data by storing only their differences to a reference sequence, thereby drastically cutting the storage cost. However, an unresolved question in this area is whether it is possible to perform search directly on the compressed data, and if so, how. Here we show that directly querying compressed genomic sequence data is possible and can be done efficiently. We describe a set of novel index structures and algorithms for this purpose, and present several optimization techniques to reduce the space requirement and query response time. We demonstrate the advantage of our method and compare it against existing ones through a thorough experimental study on real genomic data.

To facilitate review and management of home videos captured by mobile phones, we propose a novel instant summarization method which is applied while users are shooting. Segment boundaries and key frames are extracted without any delay, which means that the extracted frames strictly synchronize with the scene being captured. Partial-context is the major challenge of this method since only captured frames are available when summarization is applied. And limited calculation resource of mobile phones is another restricted condition in such a video analysis, especially when video compression is executed meanwhile. Several frame features are utilized for segmentation and key frame extraction; and an original key frame updating strategy is presented to optimize selected representative frames in such partial-context. Experimental results demonstrate that the proposed method is satisfactory in the aspects of low computation complexity, high effectiveness and good user experience.