The lack of efficiency in neural information retrieval remains one of the primary obstacles to deploying neural retrieval models as a first-stage retriever at scale. While recent tools have improved the standardized measurement of model efficiency, substantial progress is still needed to enable systematic comparative evaluation, for example, in terms of standards for systems and hardware configurations, cloud-based evaluation, benchmarks, and reproducibility. Beyond measurement, the IR community also needs stronger incentives to move in this direction, such as cost-efficiency as a review criterion or as efficiency and/or effectiveness measures in shared tasks, related teaching materials, efficiency-oriented user studies, and specialized awards for efficiency achievements. In particular, developing more efficient variants of highly effective retrieval algorithms should become an admissible research goal for PhD students if cost-efficiency is to become a first-class design objective in~IR. With ReNeuIR, we have established a recurring forum where these questions and new ideas are discussed and where the community comes together to collaboratively evaluate and improve efficiency benchmarking frameworks---most notably through the organization of a shared task focused on efficiency and reproducibility.

Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs substantial computational and memory costs. Current solutions---based on the well-known κ-means clustering algorithm---group similar vectors together to enable both effective compression and efficient retrieval. However, standard κ-means scales poorly with the number of clusters and dataset size, and favours frequent tokens during training while underrepresenting rare, discriminative ones. In this work, we introduce Tachiom, a multivector retrieval system that exploits token-level structure to significantly accelerate both clustering and retrieval. By accounting for tokens' distribution during centroid allocation, Tachiom easily scales to millions of centroids, enabling highly accurate document scoring using only centroids, avoiding expensive token-level computation. Tachiom combines a graph-based index over centroids with an optimized Product Quantization layout for efficient final scoring. Experiments on Ms Marco-v1 and LoTTE show that Tachiom achieves up to 247× faster clustering than κ-means and up to 9.8× retrieval speedup over state-of-the-art systems while maintaining comparable or superior effectiveness.

State-of-the-art Learned Sparse Retrieval (LSR) models, such as \splade, typically employ a Language Modeling (LM) head to project latent hidden states into a lexically-anchored logit matrix. This intermediate matrix is subsequently transformed into a sparse lexical representation through element-wise operations (ReLU, łogp) and max-pooling over the sequence dimension. Despite its effectiveness, the LM head creates a massive memory bottleneck due to the sheer size of the vocabulary (V ), which can range from 30,000 to over 250,000 tokens in recent models. Materializing this matrix creates a significant memory bottleneck, limiting model scaling. The resulting I/O overhead between operators further throttles throughput and runtime performance. In this paper, we propose SPARTON, a fast---memory-efficient---Triton kernel tailored for the LM head in LSR models. SPARTON utilizes a fused approach that integrates the tiled matrix multiplication, ReLU, Log1P, and max-reduction into a single GPU kernel. By performing an early online reduction directly on raw logit tiles, SPARTON avoids materializing the full logit matrix in memory. Our experiments demonstrate that the SPARTON kernel, in isolation, achieves up to a 4.8× speedup and an order-of-magnitude reduction in peak memory usage compared to PyTorch baselines. Integrated into SPLADE (|V | ≈ 30k ), SPARTON enables a 33% larger batch size and 14% faster training with no effectiveness loss. On a multilingual backbone (|V | ≈ 250k ), these gains jump to a 26× larger batch size and 2.5× faster training.

Measuring effectiveness and efficiency in information retrieval has a strong empirical background. While modern retrieval systems substantially improve effectiveness, the community has not yet agreed on how to measure efficiency, making it difficult to contrast effectiveness and efficiency fairly. Efficiency-oriented system comparisons are difficult due to factors such as hardware configurations, software versioning, and experimental settings. Efficiency affects users, researchers, and the environment and can be measured in many dimensions beyond time and space, such as resource consumption, water usage, and sample efficiency. Analyzing the efficiency of algorithms and their trade-off with effectiveness requires revisiting and establishing new standards and principles, from defining relevant concepts to designing new measures and guidelines to assess the findings' significance. ReNeuIR's fourth iteration aims to bring the community together to debate these questions and collaboratively test and improve benchmarking frameworks for efficiency based on discussions and collaborations of its previous iterations, including a shared task focused on efficiency and reproducibility.

Conversational Information Access systems have experienced widespread diffusion thanks to the natural and effortless interactions they enable with the user. In particular, they represent an effective interaction interface for conversational search (CS) and conversational recommendation (CR) scenarios. Despite their commonalities, CR and CS systems are often devised, developed, and evaluated as isolated components. Integrating these two elements would allow for handling complex information access scenarios, such as exploring unfamiliar recommended product aspects, enabling richer dialogues, and improving user satisfaction. As of today, the scarce availability of integrated datasets - focused exclusively on either of the tasks - limits the possibilities for evaluating by-design integrated CS and CR systems. To address this gap, we propose CoSRec, the first dataset for joint Conversational Search and Recommendation (CSR) evaluation. The CoSRec test set includes 20 high-quality conversations, with human-made annotations for the quality of conversations, and manually crafted relevance judgments for products and documents. Additionally, we provide supplementary training data comprising partially annotated dialogues and raw conversations to support diverse learning paradigms. CoSRec is the first resource to model CR and CS tasks in a unified framework, enabling the training and evaluation of systems that must shift between answering queries and making suggestions dynamically.

Pre-trained language models based on transformer networks are highly effective for document re-ranking in ad-hoc search. Among these, cross-encoders stand out for their effectiveness, as they process query-document pairs through the entire transformer network to compute ranking scores. However, this traversal is computationally expensive. To address this, prior work has explored early-exit strategies, enabling the model to terminate the traversal of query-document pairs. These techniques rely on learned classifiers, placed after each transformer block, that decide if a query-document pair can be dropped. Diverging from previous approaches, we propose Similarity-based Early Exit (SEE), a novel-non-learned-strategy that exploits the similarities between query and document token embeddings to early-terminate the inference of documents that will most likely be non-relevant to the query. Even though SEE can be used after every transformer block, we show that the best advantage is achieved when applied before the first transformer block, thus saving most of the inference cost for the query-document pairs. Reproducible experiments on 17 public datasets covering in-domain and out-of-domain evaluation show that SEE can be effectively applied to four different cross-encoders, achieving speedups of up to 3.5× with a limited loss in ranking effectiveness.

Approximate Nearest Neighbors (ANN) search is a core task in Information Retrieval. However, the high computational demands and reliance on expensive infrastructures limit broader contributions to ANN research. Enabling efficient and effective ANN search on low-resource devices would allow researchers in low-income countries to participate in the ANN community, thereby democratizing the field. Despite its potential, the IR literature offers little work on the feasibility of ANN search under resource constraints. In this proposal, we explore efficient solutions for large-scale ANN search on low-resource devices. We report a preliminary experimentation highlighting current limitations and outlining future challenges.

Pre-trained language models have been widely exploited to learn dense representations of documents and queries for information retrieval. While previous efforts have primarily focused on improving effectiveness and user satisfaction, response time remains a critical bottleneck of conversational search systems. To address this, we exploit the topical locality inherent in conversational queries, i.e., the tendency of queries within a conversation to focus on related topics. By leveraging query embedding similarities, we dynamically restrict the search space to semantically relevant document clusters, reducing computational complexity without compromising retrieval quality. We evaluate our approach on the TREC CAsT, 2019 and 2020 datasets using multiple embedding models and vector indexes, achieving improvements in processing speed of up to 10.3X with little loss in performance (4.3X without any loss). Our results show that the proposed system effectively handles complex, multi-turn queries with high precision and efficiency, offering a practical solution for real-time conversational search.

Learned Sparse Retrieval (LSR) is an effective IR approach that exploits pre-trained language models for encoding text into a learned bag of words. Several efforts in the literature have shown that sparsity is key to enabling a good trade-off between the efficiency and effectiveness of the query processor. To induce the right degree of sparsity, researchers typically use regularization techniques when training LSR models. Recently, new efficient-inverted index-based-retrieval engines have been proposed, leading to a natural question: has the role of regularization changed in training LSR models? In this paper, we conduct an extended evaluation of regularization approaches for LSR where we discuss their effectiveness, efficiency, and out-of-domain generalization capabilities. We first show that regularization can be relaxed to produce more effective LSR en- coders. We also show that query encoding is now the bottleneck limiting the overall query processor performance. To remove this bottleneck, we advance the state-of-the-art of inference-free LSR by proposing Learned Inference-free Retrieval (Li-Lsr). At training time, Li-Lsr learns a score for each token, casting the query encoding step into a seamless table lookup. Our approach yields state-of-the-art effectiveness for both in-domain and out-of-domain evaluation,surpassing Splade-v3-Doc by 1 point of mRR@10 on MsMarco and 1.8 points of nDCG@10 on Beir.

Quantization and pruning are two effective Deep Neural Network model compression methods. In this paper, we propose Automatic Prune Binarization ( APB ), a novel compression technique combining quantization with pruning. APB enhances the representational capability of binary networks using a few full-precision weights. Our technique jointly maximizes the accuracy of the network while minimizing its memory impact by deciding whether each weight should be binarized or kept in full precision. We show how to efficiently perform a forward pass through layers compressed using APB by decomposing it into a binary and a sparse-dense matrix multiplication. Moreover, we design two novel efficient algorithms for extremely quantized matrix multiplication on CPU, leveraging highly efficient bitwise operations. The proposed algorithms are 6.9× and 1.5× faster than available state-of-the-art solutions. We extensively evaluate APB on two widely adopted model compression datasets, namely CIFAR-10 and ImageNet. APB shows to deliver better accuracy/memory trade-off compared to state-of-the-art methods based on i) quantization, ii) pruning, and iii) a combination of pruning and quantization. APB also outperforms quantization in the accuracy/efficiency trade-off, being up to 2× faster than the 2-bits quantized model with no loss in accuracy. • A network compression method ( APB ) that combines pruning and quantization. • Two matrix multiplication algorithms for extremely low-bit operands. • APB reduces the network size by one order of magnitude with no accuracy loss.

Learning ranking models that are both explainable and effective is an emerging topic within the research area of explainable AI. Several Learning-to-Rank (LtR) algorithms have been recently proposed that build models that are simple to explain and, at the same time, almost as effective as their state-of-the-art, black-box counterparts. In this work, we propose Interpretable LambdaMART (ILMART), a novel framework with different strategies to constrain the state-of-the-art LtR LambdaMART algorithm to generate interpretable models, i.e., ensembles whose trees can use either single features (main effects) or a limited number of interacting features (interaction effects). ILMART facilitates a straightforward tradeoff between model explainability and effectiveness by precisely tuning the quantity of main and interaction effects during the learning phase. We show that slightly increasing their number allows ILMART models to reach ranking performances at par with full-complexity LambdaMART ones. Furthermore, reproducible experiments conducted on publicly available LtR datasets demonstrate that ILMART can improve nDCG@10 by up to 10% compared to state-of-the-art competitors while preserving an explainable structure. Finally, we explore the relationship between model explainability and inference efficiency by introducing a novel and easy-to-implement scoring algorithm for ILMART ranking models, achieving up to a \(100\times\) speedup compared to the baseline.

Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challenges, a recent algorithm called Seismic has enabled remarkably fast, highly-accurate approximate retrieval. Seismic statically prunes inverted lists, organizes each list into geometrically-cohesive blocks, and augments each block with a summary vector. At query time, each inverted list associated with a query term is traversed one block at a time in an arbitrary order, with the inner product between the query and summaries determining if a block must be evaluated. When a block is deemed promising, its documents are fully evaluated with a forward index. Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and significantly outperforms the winning graph-based submissions to the BigANN 2023 Challenge. In this work, we speed up Seismic further by introducing two innovations to its query processing subroutine. First, we traverse blocks in order of importance, rather than arbitrarily. Second, we take the list of documents retrieved by Seismic and expand it to include the neighbors of each document using an offline k-regular nearest neighbor graph; the expanded list is then ranked to produce the final top-k set. Experiments on two public datasets show that our extension, named SeismicWave, can reach almost-exact accuracy levels and is up to 2.2x faster than Seismic.

Learned dense representations are a popular family of techniques for encoding queries and documents using high-dimensional embeddings, which enable retrieval by performing approximate k nearest-neighbors search (A-kNN). A popular technique for making A-kNN search efficient is based on a two-level index, where the embeddings of documents are clustered offline and, at query processing, a fixed number N of clusters closest to the query is visited exhaustively to compute the result set. In this paper, we build upon state-of-the-art for early exit A-kNN and propose an unsupervised method based on the notion of patience, which can reach competitive effectiveness with large efficiency gains. Moreover, we discuss a cascade approach where we first identify queries that find their nearest neighbor within the closest t << N clusters, and then we decide how many more to visit based on our patience approach or other state-of-the-art strategies. Reproducible experiments employing state-of-the-art dense retrieval models and publicly available resources show that our techniques improve the A-kNN efficiency with up to 5x speedups while achieving negligible effectiveness losses. All the code used is available at https://github.com/francescobusolin/faiss_pEE

Recent studies in Learning to Rank (LtR) have shown the possibility of effectively distilling a neural network from an ensemble of regression trees. This fully enables the use of neural-based ranking models in query processors of modern Web search engines. Nevertheless, ensembles of regression trees outperform neural models both in terms of efficiency and effectiveness on CPU. In this paper, we propose a framework to design and train neural networks outperforming ensembles of regression trees. After distilling the networks from tree-based models, we exploit an efficiency-oriented pruning technique that works by sparsifying the most computationally intensive layers of the model. Moreover, we develop inference time predictors, which help devise neural network architectures that match the desired efficiency requirements. Comprehensive experiments on two public learning-to-rank datasets show that the neural networks produced with our novel approach are competitive in terms of effectiveness-efficiency trade-off when compared with tree-based ensembles by providing up to 4x inference time speed-up without degradation of the ranking quality.

Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable by design. Despite their apparent compatibility with inverted indexes, however, retrieval over sparse embeddings remains challenging. That is due to the distributional differences between learned embeddings and term frequency-based lexical models of relevance such as BM25. Recognizing this challenge, a great deal of research has gone into, among other things, designing retrieval algorithms tailored to the properties of learned sparse representations, including approximate retrieval systems. In fact, this task featured prominently in the latest BigANN Challenge at NeurIPS 2023, where approximate algorithms were evaluated on a large benchmark dataset by throughput and recall. In this work, we propose a novel organization of the inverted index that enables fast yet effective approximate retrieval over learned sparse embeddings. Our approach organizes inverted lists into geometrically-cohesive blocks, each equipped with a summary vector. During query processing, we quickly determine if a block must be evaluated using the summaries. As we show experimentally, single-threaded query processing using our method, Seismic, reaches sub-millisecond per-query latency on various sparse embeddings of the MS MARCO dataset while maintaining high recall. Our results indicate that Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and further outperforms the winning (graph-based) submissions to the BigANN Challenge by a significant margin.

A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of k data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set. One popular approach to this question is clustering: The indexing algorithm partitions data points into non-overlapping subsets and represents each partition by a point such as its centroid. The query processing algorithm first identifies the nearest clusters---a process known as routing---then performs a nearest neighbor search over those clusters only. In this work, we make a simple observation: The routing function solves a ranking problem. Its quality can therefore be assessed with a ranking metric, making the function amenable to learning-to-rank. Interestingly, ground-truth is often freely available: Given a query distribution in a top-k configuration, the ground-truth is the set of clusters that contain the exact top-k vectors. We develop this insight and apply it to Maximum Inner Product Search (MIPS). As we demonstrate empirically on various datasets, learning a simple linear function consistently improves the accuracy of clustering-based MIPS.

Abstract State-of-the-art Learning-to-Rank algorithms, e.g., $$\lambda$$ λ MART, rely on univariate scoring functions to score a list of items. Univariate scoring functions score each item independently, i.e., without considering the other available items in the list. Nevertheless, ranking deals with producing an effective ordering of the items and comparisons between items are helpful to achieve this task. Bivariate scoring functions allow the model to exploit dependencies between the items in the list as they work by scoring pairs of items. In this paper, we exploit item dependencies in a novel framework—we call it the Lambda Bivariate (LB) framework—that allows to learn effective bivariate scoring functions for ranking using gradient boosting trees. We discuss the three main ingredients of LB: (i) the invariance to permutations property, (ii) the function aggregating the scores of all pairs into the per-item scores, and (iii) the optimization process to learn bivariate scoring functions for ranking using any differentiable loss functions. We apply LB to the $$\lambda$$ λ Rank loss and we show that it results in learning a bivariate version of $$\lambda$$ λ MART—we call it Bi- $$\lambda$$ λ MART—that significantly outperforms all neural-network-based and tree-based state-of-the-art algorithms for Learning-to-Rank. To show the generality of LB with respect to other loss functions, we also discuss its application to the Softmax loss.

The aim of this Special Section is to engage with researchers in Information Retrieval, Natural Language Processing and related areas and gather insight into the core challenges in measuring, reporting, and optimizing all facets of efficiency in Neural Information Retrieval (NIR) systems, including time-, space-, resource-, sample-, and energy-efficiency, among other factors. While researchers in the field have assiduously explored the Pareto frontier in quality and efficiency in other contexts for decades, we believe that the neural dimension introduces new hurdles.

Maximum Inner Product Search or top- k retrieval on sparse vectors is well understood in information retrieval, with a number of mature algorithms that solve it exactly. However, all existing algorithms are tailored to text and frequency-based similarity measures. To achieve optimal memory footprint and query latency, they rely on the near stationarity of documents and on laws governing natural languages. We consider, instead, a setup in which collections are streaming—necessitating dynamic indexing—and where indexing and retrieval must work with arbitrarily distributed real-valued vectors. As we show, existing algorithms are no longer competitive in this setup, even against naïve solutions. We investigate this gap and present a novel approximate solution, called Sinnamon , that can efficiently retrieve the top- k results for sparse real valued vectors drawn from arbitrary distributions. Notably, Sinnamon offers levers to trade off memory consumption, latency, and accuracy, making the algorithm suitable for constrained applications and systems. We give theoretical results on the error introduced by the approximate nature of the algorithm and present an empirical evaluation of its performance on two hardware platforms and synthetic and real-valued datasets. We conclude by laying out concrete directions for future research on this general top- k retrieval problem over sparse vectors.

Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top- \(k\) retrieval in Information Retrieval. This duality exists because sparse and dense vectors serve different end goals. That is despite the fact that they are manifestations of the same mathematical problem. In this work, we ask if algorithms for dense vectors could be applied effectively to sparse vectors, particularly those that violate the assumptions underlying top- \(k\) retrieval methods. We study clustering-based approximate MIPS where vectors are partitioned into clusters and only a fraction of clusters are searched during retrieval. We conduct a comprehensive analysis of dimensionality reduction for sparse vectors, and examine standard and spherical k -means for partitioning. Our experiments demonstrate that clustering-based retrieval serves as an efficient solution for sparse MIPS. As byproducts, we identify two research opportunities and explore their potential. First, we cast the clustering-based paradigm as dynamic pruning and turn that insight into a novel organization of the inverted index for approximate MIPS over general sparse vectors. Second, we offer a unified regime for MIPS over vectors that have dense and sparse subspaces, that is robust to query distributions.

Rapid response, namely, low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documents retrieved. Motivated by this observation, we propose and evaluate a client-side document embedding cache, improving the responsiveness of conversational search systems. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index, answering nearest-neighbor similarity queries by estimating the approximate result sets returned. We demonstrate the efficiency achieved using our cache via reproducible experiments based on Text Retrieval Conference Conversational Assistant Track datasets, achieving a hit rate of up to 75% without degrading answer quality. Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.

Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP).

Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier in IR and RSs. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution. PDU considers fine-grained utopia points, and measures how far each point is from its utopia point, allowing to select solutions tailored to user preferences, a novel feature we call "calibration". We compare PDU against state-of-the-art strategies through extensive experiments on tasks from both IR and RS, showing that PDU combined with calibration notably impacts the solution selection.

Multifaceted, empirical evaluation of algorithmic ideas is one of the central pillars of Information Retrieval (IR) research. The IR community has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. As the community moves towards even more complex deep learning models, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time and space; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment alike. Examining algorithms and models through the lens of holistic efficiency requires the establishment of standards and principles, from defining relevant concepts, to designing metrics, to creating guidelines for making sense of the significance of new findings. The second iteration of the ReNeuIR workshop aims to bring the community together to debate these questions, with the express purpose of moving towards a common benchmarking framework for efficiency.

A tournament graph is a complete directed graph, which can be used to model a round-robin tournament between$n$players. In this paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches. In detail, we aim to investigate algorithms that find the champion by playing a low number of matches. Solving this problem allows us to speed up several Information Retrieval and Recommender System applications, including question answering, conversational search, etc. Indeed, these applications often search for the champion inducing a round-robin tournament among the players by employing a machine learning model to estimate who wins each pairwise comparison. Our contribution, thus, allows finding the champion by performing a low number of model inferences. We prove that any deterministic or randomized algorithm finding a champion with constant success probability requires$\Omega (\ell n)$comparisons, where$\ell$is the number of matches lost by the champion. We then present an asymptotically-optimal deterministic algorithm matching this lower bound without knowing$\ell$, and we extend our analysis to three variants of the problem. Lastly, we conduct a comprehensive experimental assessment of the proposed algorithms on a question answering task on public data. Results show that our proposed algorithms speed up the retrieval of the champion up to$13\times$with respect to the state-of-the-art algorithm that perform the full tournament. The identification of the most relevant result from a set of candidates is a crucial task in many Information Retrieval and Recommender System applications including ad-hoc search, conversational search, machine translation, question answering, etc. State-of-the-art solutions solving the task leverage ad-hoc machine learning techniques—developed in a field known as Learning-to-Rank—to estimate the relevance of the set of candidate results and to select the most relevant one. These solutions address the problem in two different ways. From one side, several techniques work by estimating one candidate at a time so to select the candidate achieving the highest score. On the other side, some techniques compare a pair of candidate results at a time so to select the candidate achieving the highest sum of pairwise scores of an all-vs-all tournament between results. In this paper, we focus on the second class of techniques. In detail, we propose to model the task of identifying the most relevant result from a set of candidates as the problem of finding the champion of a tournament, which is the player that wins the highest number of matches in the tournament. Our goal is to find the champion, also known as theCopeland winner, of the tournament by minimizing the number of matches played in the tournament, i.e., the number of pairwise comparisons. We prove that any deterministic or randomized algorithm finding a champion with constant success probability requires$\Omega (\ell n)$comparisons, where$\ell$is the number of matches lost by the champion. We then present an asymptotically-optimal deterministic algorithm matching this lower bound without knowing$\ell$. Moreover, we extend the result by providing a parallel version of our algorithm, as well as a version that retrieve all the best$k$players of the tournament. Lastly, we conduct a comprehensive experimental assessment of the proposed algorithms on a public dataset (MS-MARCO) with the aim of speeding up a well-known state-of-the-art solution for ranking textual passages for question answering. Results show that our proposed solutions allow to speed up the identification of the champion up to$13\times$.

Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking task. Nevertheless, ensembles of regression trees outperform neural models both in terms of efficiency and effectiveness, particularly when scoring on CPU. In this paper, we propose an approach for speeding up neural scoring time by applying a combination of Distillation, Pruning and Fast Matrix multiplication. We employ knowledge distillation to learn shallow neural networks from an ensemble of regression trees. Then, we exploit an efficiency-oriented pruning technique that performs a sparsification of the most computationally-intensive layers of the neural network that is then scored with optimized sparse matrix multiplication. Moreover, by studying both dense and sparse high performance matrix multiplication, we develop a scoring time prediction model which helps in devising neural network architectures that match the desired efficiency requirements. Comprehensive experiments on two public learning-to-rank datasets show that neural networks produced with our novel approach are competitive at any point of the effectiveness-efficiency trade-off when compared with tree-based ensembles, providing up to 4x scoring time speed-up without affecting the ranking quality.

Perhaps the applied nature of information retrieval research goes some way to explain the community's rich history of evaluating machine learning models holistically, understanding that efficacy matters but so does the computational cost incurred to achieve it. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in learning-to-rank. As the community adopts even more complex, neural network-based models in a wide range of applications, questions on efficiency have once again become relevant. We propose this workshop as a forum for a critical discussion of efficiency in the era of neural information retrieval, to encourage debate on the current state and future directions of research in this space, and to promote more sustainable research by identifying best practices in the development and evaluation of neural models for information retrieval.

Neural approaches that use pre-trained language models are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their effectiveness compared to feature-based Learning-to-Rank (LtR) methods has not yet been well-established. A major reason for this is because present LtR benchmarks that contain query-document feature vectors do not contain the raw query and document text needed for neural models. On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. The dataset consists of a comprehensive corpus of 8.4M web documents, a collection of query-document pairs including 220 hand-crafted features, relevance judgments on a 5-graded scale, and a set of 2,198 textual queries used for testing purposes. Istella22 enables a fair evaluation of traditional learning-to-rank and transfer ranking techniques on the same data. LtR models exploit the feature-based representations of training samples while pre-trained transformer-based neural rankers can be evaluated on the corresponding textual content of queries and documents. Through preliminary experiments on Istella22, we find that neural re-ranking approaches lag behind LtR models in terms of effectiveness. However, LtR models identify the scores from neural models as strong signals.

Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.

Modern search services often provide multiple options to rank the search results, e.g., sort “by relevance”, “by price” or “by discount” in e-commerce. While the traditional rank by relevance effectively places the relevant results in the top positions of the results list, the rank by attribute could place many marginally relevant results in the head of the results list leading to poor user experience. In the past, this issue has been addressed by investigating the relevance-aware filtering problem, which asks to select the subset of results maximizing the relevance of the attribute-sorted list. Recently, an exact algorithm has been proposed to solve this problem optimally. However, the high computational cost of the algorithm makes it impractical for the Web search scenario, which is characterized by huge lists of results and strict time constraints. For this reason, the problem is often solved using efficient yet inaccurate heuristic algorithms. In this article, we first prove the performance bounds of the existing heuristics. We then propose two efficient and effective algorithms to solve the relevance-aware filtering problem. First, we propose OPT-Filtering, a novel exact algorithm that is faster than the existing state-of-the-art optimal algorithm. Second, we propose an approximate and even more efficient algorithm, ϵ-Filtering, which, given an allowed approximation error ϵ, finds a (1-ϵ)–optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of the two proposed algorithms against state-of-the-art competitors on two real-world public datasets. Experimental results show that OPT-Filtering achieves a significant speedup of up to two orders of magnitude with respect to the existing optimal solution, while ϵ-Filtering further improves this result by trading effectiveness for efficiency. In particular, experiments show that ϵ-Filtering can achieve quasi-optimal solutions while being faster than all state-of-the-art competitors in most of the tested configurations.

Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the average number of trees traversed by documents to accumulate the scores, thus reducing the overall query response time. LEAR exploits a classifier that predicts whether a document can early exit the ensemble because it is unlikely to be ranked among the final top-k results. The early exit decision occurs at a sentinel point, i.e., after having evaluated a limited number of trees, and the partial scores are exploited to filter out non-promising documents. We evaluate LEAR by deploying it in a production-like setting, adopting a state-of-the-art algorithm for ensembles traversal. We provide a comprehensive experimental evaluation on two public datasets. The experiments show that LEAR has a significant impact on the efficiency of the query processing without hindering its ranking quality. In detail, on a first dataset, LEAR is able to achieve a speedup of 3x without any loss in [email protected], while on a second dataset the speedup is larger than 5x with a negligible [email protected] loss (< 0.05%).

We observe that in curated documents the distribution of the occurrences of salient terms, e.g., terms with a high Inverse Document Frequency, is not uniform, and such terms are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of the document. We study a multiplicity of partitioning schemes of document content into passages and compute the collection-dependent weights associated with them on the basis of the distribution of occurrences of salient terms in documents. Moreover, we tune BM25P hyperparameters and investigate their impact on ad hoc document retrieval through fully reproducible experiments conducted using four publicly available datasets. Our findings demonstrate that our BM25P weighting model markedly and consistently outperforms BM25 in terms of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1, and up to 21% in MRR.

In this paper, we study the problem of predicting the next position of a tourist given his history. In particular, we propose a model to identify the next point of interest that a tourist will visit in the future, by making use of similarity between trajectories on a graph and taking into account the spatial-temporal aspect of trajectories. We compare our method with a well-known machine learning-based technique, as well as with a popularity baseline, using three public real-world datasets. Our experimental results show that our technique outperforms state-of-the-art machine learning-based methods effectively, by providing at least twice more accurate results.

Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers to investigate algorithms to make faster the traversal of the additive ensemble or to early terminate the evaluation of documents that are unlikely to be ranked among the top-k. In this paper, we investigate the novel problem of query-level early exiting, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble. Besides the obvious advantage on query latency and throughput, we address the possible positive impact on ranking effectiveness. To this end, we study the actual contribution of incremental portions of the tree ensemble to the ranking of the top-k documents scored for a given query. Our main finding is that queries exhibit different behaviors as scores are accumulated during the traversal of the ensemble and that query-level early stopping can remarkably improve ranking quality. We present a reproducible and comprehensive experimental evaluation, conducted on two public datasets, showing that query-level early exiting achieves an overall gain of up to 7.5% in terms of [email protected] with a speedup of the scoring process of up to 2.2x.

Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them cost-prohibitive in practice. Our proposed approach, called PreTTR (Precomputing Transformer Term Representations), considerably reduces the query-time latency of deep transformer networks (up to a 42x speedup on web document ranking) making these networks more practical to use in a real-time ranking scenario. Specifically, we precompute part of the document term representations at indexing time (without a query), and merge them with the query representation at query time to compute the final ranking score. Due to the large size of the token representations, we also propose an effective approach to reduce the storage requirement by training a compression layer to match attention scores. Our compression technique reduces the storage required up to 95% and it can be applied without a substantial degradation in ranking performance.

In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify the easiest correct answers first (i.e., assign a high ranking score to answers that have characteristics that usually indicate relevance, and a low ranking score to those with characteristics that do not), before incorporating more complex logic to handle difficult cases (e.g., semantic matching or reasoning). In this work, we apply this idea to the training of neural answer rankers using curriculum learning. We propose several heuristics to estimate the difficulty of a given training sample. We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process. As the training process progresses, our approach gradually shifts to weighting all samples equally, regardless of difficulty. We present a comprehensive evaluation of our proposed idea on three answer ranking datasets. Results show that our approach leads to superior performance of two leading neural ranking architectures, namely BERT and ConvKNRM, using both pointwise and pairwise losses. When applied to a BERT-based ranker, our method yields up to a 4% improvement in MRR and a 9% improvement in [email protected] (compared to the model trained without a curriculum). This results in models that can achieve comparable performance to more expensive state-of-the-art techniques.

The identification of relevance with little textual context is a primary challenge in passage retrieval. We address this problem with a representation-based ranking approach that: (1) explicitly models the importance of each term using a contextualized language model; (2) performs passage expansion by propagating the importance to similar terms; and (3) grounds the representations in the lexicon, making them interpretable. Passage representations can be pre-computed at index time to reduce query-time latency. We call our approach EPIC (Expansion via Prediction of Importance with Contextualization). We show that EPIC significantly outperforms prior importance-modeling and document expansion approaches. We also observe that the performance is additive with the current leading first-stage retrieval methods, further narrowing the gap between inexpensive and cost-prohibitive passage ranking approaches. Specifically, EPIC achieves a [email protected] of 0.304 on the MS-MARCO passage ranking dataset with 78ms average query latency on commodity hardware. We also find that the latency is further reduced to 68ms by pruning document representations, with virtually no difference in effectiveness.

In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the conversation evolves through user utterances and system replies. The retrieval of documents relevant to a given utterance in a conversation is challenging due to ambiguity of natural language and to the difficulty of detecting possible topic shifts and semantic relationships among utterances. We adopt the 2019 TREC Conversational Assistant Track (CAsT) framework to experiment with a modular architecture performing: (i) topic-aware utterance rewriting, (ii) retrieval of candidate passages for the rewritten utterances, and (iii) neural-based re-ranking of candidate passages. We present a comprehensive experimental evaluation of the architecture assessed in terms of traditional IR metrics at small cutoffs. Experimental results show the effectiveness of our techniques that achieve an improvement of up to $0.28$ (+93%) for [email protected] and $0.19$ (+89.9%) for [email protected] w.r.t. the CAsT baseline.

We observe that most relevant terms in unstructured news articles are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of news articles. Our experimentation, conducted using three publicly available news datasets, demonstrates that BM25P markedly outperforms BM25 in term of effectiveness by up to 17.44% in [email protected] and 85% in [email protected]

This tutorial aims to weave together diverse strands of modern Learning to Rank (LtR) research, and present them in a unified full-day tutorial. First, we will introduce the fundamentals of LtR, and an overview of its various sub-fields. Then, we will discuss some recent advances in gradient boosting methods such as LambdaMART by focusing on their efficiency/effectiveness trade-offs and optimizations. Subsequently, we will then present TF-Ranking, a new open source TensorFlow package for neural LtR models, and how it can be used for modeling sparse textual features. Finally, we will conclude the tutorial by covering unbiased LtR -- a new research field aiming at learning from biased implicit user feedback. The tutorial will consist of three two-hour sessions, each focusing on one of the topics described above. It will provide a mix of theoretical and hands-on sessions, and should benefit both academics interested in learning more about the current state-of-the-art in LtR, as well as practitioners who want to use LtR techniques in their applications.

Several Web search services enable their users with the possibility of sorting the list of results by a specific attribute, e.g., sort "by price" in e-commerce. However, sorting the results by attribute could bring marginally relevant results in the top positions thus leading to a poor user experience. This motivates the definition of the relevance-aware filtering problem. This problem asks to remove results from the attribute-sorted list to maximize its final overall relevance. Recently, an optimal solution to this problem has been proposed. However, it has strong limitations in the Web scenario due to its high computational cost. In this paper, we propose ϵ-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list. More precisely, given an allowed approximation error ϵ, the proposed algorithm finds a(1-ϵ)"optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of ϵ-Filtering against state-of-the-art competitors on two real-world public datasets. Experiments show that ϵ-Filtering achieves the desired levels of effectiveness with a speedup of up to two orders of magnitude with respect to the optimal solution while guaranteeing very small approximation errors.

Query Expansion (QE) techniques expand the user queries with additional terms, e.g., synonyms and acronyms, to enhance the system recall. State-of-the-art solutions employ machine learning methods to select the most suitable terms. However, most of them neglect the cost of processing the expanded queries, thus selecting effective, yet very expensive, terms. The goal of this paper is to enable QE in scenarios with tight time constraints proposing a QE framework based on structured queries and efficiency-aware term selection strategies. In particular, the proposed expansion selection strategies aim at capturing the efficiency and the effectiveness of the expansion candidates, as well as the dependencies among them. We evaluate our proposals by conducting an extensive experimental assessment on real-world search engine data and public TREC data. Results confirm that our approach leads to a remarkable efficiency improvement w.r.t. the state-of-the-art: a reduction of the retrieval time up to 30 times, with only a small loss of effectiveness.

Learning an effective ranking function from a large number of query-document examples is a challenging task. Indeed, training sets where queries are associated with a few relevant documents and a large number of irrelevant ones are required to model real scenarios of Web search production systems, where a query can possibly retrieve thousands of matching documents, but only a few of them are actually relevant. In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely hindering the quality of the learned model. SelGB exploits a novel technique minimizing the mis-ranking risk, i.e., the probability that two randomly drawn instances are ranked incorrectly, within a gradient boosting process that iteratively generates an additive ensemble of decision trees. Specifically, at every iteration and on a per query basis, SelGB selectively chooses among the training instances a small sample of negative examples enhancing the discriminative power of the learned model. Reproducible and comprehensive experiments conducted on a publicly available dataset show that SelGB exploits the diversity and variety of the negative examples selected to train tree ensembles that outperform models generated by state-of-the-art algorithms by achieving improvements of [email protected] up to 3.2%.

Learning-to-Rank (LtR) solutions are commonly used in large-scale information retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradient boosting approach to build additive ensembles of weighted regression trees. Since the required ranking effectiveness is achieved with very large ensembles, the impact on response time and query throughput of these solutions is not negligible. In this article, we propose X-CLE a VER, an iterative meta-algorithm able to build more efficient and effective ranking ensembles. X-CLE a VER interleaves the iterations of a given gradient boosting learning algorithm with pruning and re-weighting phases. First, redundant trees are removed from the given ensemble, then the weights of the remaining trees are fine-tuned by optimizing the desired ranking quality metric. We propose and analyze several pruning strategies and we assess their benefits showing that interleaving pruning and re-weighting phases during learning is more effective than applying a single post-learning optimization step. Experiments conducted using two publicly available LtR datasets show that X-CLE a VER can be successfully exploited on top of several LtR algorithms as it is effective in optimizing the effectiveness of the learnt ensembles, thus obtaining more compact forests that hence are much more efficient at scoring time.

In this demo paper we propose RankEval, an open-source tool for the analysis and evaluation of Learning-to-Rank (LtR) models based on ensembles of regression trees. Gradient Boosted Regression Trees (GBRT) is a flexible statistical learning technique for classification and regression at the state of the art for training effective LtR solutions. Indeed, the success of GBRT fostered the development of several open-source LtR libraries targeting efficiency of the learning phase and effectiveness of the resulting models. However, these libraries offer only very limited help for the tuning and evaluation of the trained models. In addition, the implementations provided for even the most traditional IR evaluation metrics differ from library to library, thus making the objective evaluation and comparison between trained models a difficult task. RankEval addresses these issues by providing a common ground for LtR libraries that offers useful and interoperable tools for a comprehensive comparison and in-depth analysis of ranking models.

In this paper we propose X-DART, a new Learning to Rank algorithm focusing on the training of robust and compact ranking models. Motivated from the observation that the last trees of MART models impact the prediction of only a few instances of the training set, we borrow from the DART algorithm the dropout strategy consisting in temporarily dropping some of the trees from the ensemble while new weak learners are trained. However, differently from this algorithm we drop permanently these trees on the basis of smart choices driven by accuracy measured on the validation set. Experiments conducted on publicly available datasets shows that X-DART outperforms DART in training models providing the same effectiveness by employing up to 40% less trees.

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.

Several among our daily activities, like traveling to a tourist attraction, are better enjoyed with a group of friends. However, finding the best travel companions is sometimes tricky since we need to form a group of people combining the interest in the proposed destination with the friendship relations among the group members. In this paper we cope with this problem by proposing a new method to recommend the best group of friends with whom to enjoy a specific item, i.e., a travel destination or a venue to visit. Our approach provides a new and original perspective on recommendation: given a user, her social network and a recommended item that is relevant for the user, we want to suggest the best group of friends with whom enjoying the item. This approach differs from traditional group recommendation since it tries to maximize two orthogonal aspects: i) the relevance of the recommended item for every member of the group, and ii), the intra-group social relationships. We introduce the Group Finder framework defining the User-Item Group Formation problem and the possible solutions. We assess our approach in the domain of location recommendation and experiment the proposed solutions using four different publicly available Location Based Social Network (LBSN) datasets. The results achieved confirm the effectiveness and the feasibility of the proposed solutions that outperform strong baselines.

Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ranking functions from a ground-truth of training examples. In practice, efficiency and effectiveness are intertwined concepts and trading off effectiveness for meeting efficiency constraints typically existing in large-scale systems is one of the most urgent issues. In this paper we propose a new framework, named CLEaVER, for optimizing machine-learned ranking models based on ensembles of regression trees. The goal is to improve efficiency at document scoring time without affecting quality. Since the cost of an ensemble is linear in its size, CLEaVER first removes a subset of the trees in the ensemble, and then fine-tunes the weights of the remaining trees according to any given quality measure. Experiments conducted on two publicly available LtR datasets show that CLEaVER is able to prune up to 80% of the trees and provides an efficiency speed-up up to 2.6x without affecting the effectiveness of the model.

Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is currently deemed one of the best solutions to effectively rank query results to be returned by large scale Information Retrieval systems. This paper investigates the opportunities given by SIMD capabilities of modern CPUs to the end of efficiently evaluating regression trees ensembles. We propose V-QuickScorer (vQS), which exploits SIMD extensions to vectorize the document scoring, i.e., to perform the ensemble traversal by evaluating multiple documents simultaneously. We provide a comprehensive evaluation of vQS against the state of the art on three publicly available datasets. Experiments show that vQS provides speed-ups up to a factor of 3.2x.

Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very effective for scoring query results returned by large-scale Web search engines. Unfortunately, the computational cost of scoring thousands of candidate documents by traversing large ensembles of trees is high. Thus, several works have investigated solutions aimed at improving the efficiency of document scoring by exploiting advanced features of modern CPUs and memory hierarchies. In this article, we present Q uick S corer , a new algorithm that adopts a novel cache-efficient representation of a given tree ensemble, performs an interleaved traversal by means of fast bitwise operations, and supports ensembles of oblivious trees. An extensive and detailed test assessment is conducted on two standard Learning-to-Rank datasets and on a novel very large dataset we made publicly available for conducting significant efficiency tests. The experiments show unprecedented speedups over the best state-of-the-art baselines ranging from 1.9 × to 6.6 × . The analysis of low-level profiling traces shows that Q uick S corer efficiency is due to its cache-aware approach in terms of both data layout and access patterns and to a control flow that entails very low branch mis-prediction rates.

Learning to Rank (LtR) is an effective machine learning methodology for inducing high-quality document ranking functions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vectors, a machine-learned function is used to reorder this set. In this paper we propose a new family of rank-based features, which extend the original feature vector associated with each query-document pair. Indeed, since they are derived as a function of the query-document pair and the full set of candidate documents to score, rank-based features provide additional information to better rank documents and return the most relevant ones. We report a comprehensive evaluation showing that rank-based features allow us to achieve the desired effectiveness with ranking models being up to 3.5 times smaller than models not using them, with a scoring time reduction up to 70%.

Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines, a scenario where quality and efficiency requirements are very demanding. Unfortunately, the computational cost of these ranking models is high. Thus, several works already proposed solutions aiming at improving the efficiency of the scoring process by dealing with features and peculiarities of modern CPUs and memory hierarchies. In this paper, we present QuickScorer, a new algorithm that adopts a novel bitvector representation of the tree-based ranking model, and performs an interleaved traversal of the ensemble by means of simple logical bitwise operations. The performance of the proposed algorithm are unprecedented, due to its cache-aware approach, both in terms of data layout and access patterns, and to a control flow that entails very low branch mis-prediction rates. The experiments on real Learning-to-Rank datasets show that QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x.

In this article, we tackle the problem of predicting the “next” geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist’s current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in recommender and trail prediction systems for tourism, as well as a popularity baseline. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions.

In this paper, we tackle the problem of predicting the "next" geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.

In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.

Collaborative content creation and annotation creates vast repositories of all sorts of media, and user-defined tags play a central role as they are a simple yet powerful tool for organizing, searching and exploring the available resources. We observe that when a user annotates a resource with a set of tags, those tags are introduced one at a time. Therefore, when the fourth tag is introduced, a knowledge represented by the previous three tags, i.e., the context in which the fourth tag is produced, is available and exploitable for generating potential correction of the current tag. This context, together with the "wisdom of the crowd" represented by the co-occurrences of tags in all the resources of the repository, can be exploited to provide interactive tag spell check and correction. We develop this idea in a framework, based on a weighted tag co-occurrence graph and on nodes relatedness measures defined on weighted neighborhoods. We test our proposal on a dataset coming from YouTube. The results show that our framework is effective as it outperforms two important baselines. We also show that it is efficient, thus enabling its use in modern tagging services.

In this paper we analyze the efficiency of various search results diversification methods. While efficacy of diversification approaches has been deeply investigated in the past, response time and scalability issues have been rarely addressed. A unified framework for studying performance and feasibility of result diversification solutions is thus proposed. First we define a new methodology for detecting when, and how, query results need to be diversified. To this purpose, we rely on the concept of "query refinement" to estimate the probability of a query to be ambiguous . Then, relying on this novel ambiguity detection method, we deploy and compare on a standard test set, three different diversification methods: IASelect, xQuAD, and OptSelect. While the first two are recent state-of-the-art proposals, the latter is an original algorithm introduced in this paper. We evaluate both the efficiency and the effectiveness of our approach against its competitors by using the standard TREC Web diversification track testbed. Results shown that OptSelect is able to run two orders of magnitude faster than the two other state-of-the-art approaches and to obtain comparable figures in diversification effectiveness.

World Wide Web content continuously grows in size and importance. Furthermore, users ask Web search engines to satisfy increasingly disparate information needs. New techniques and tools are constantly developed aimed at assisting users in the interaction with the Web search engine. Query recommender systems suggesting interesting queries to users are an example of such tools. Most query recommendation techniques are based on the knowledge of the behaviors of past users of the search engine recorded in query logs.