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Arithmetic N-gram: an efficient data compression technique Inf. Retrieval J. (IF 2.5) Pub Date : 2024-03-13 Ali Hassan, Sadaf Javed, Sajjad Hussain, Rizwan Ahmad, Shams Qazi
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Tashaphyne0.4: a new arabic light stemmer based on rhyzome modeling approach Inf. Retrieval J. (IF 2.5) Pub Date : 2023-12-14 Ra’ed M. Al-Khatib, Taha Zerrouki, Mohammed M. Abu Shquier, Amar Balla
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An in-depth analysis of passage-level label transfer for contextual document ranking Inf. Retrieval J. (IF 2.5) Pub Date : 2023-12-08 Koustav Rudra, Zeon Trevor Fernando, Avishek Anand
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Privacy-aware document retrieval with two-level inverted indexing Inf. Retrieval J. (IF 2.5) Pub Date : 2023-11-17 Yifan Qiao, Shiyu Ji, Changhai Wang, Jinjin Shao, Tao Yang
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Heterogeneous graph attention networks for passage retrieval Inf. Retrieval J. (IF 2.5) Pub Date : 2023-11-16 Lucas Albarede, Philippe Mulhem, Lorraine Goeuriot, Sylvain Marié, Claude Le Pape-Gardeux, Trinidad Chardin-Segui
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Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems Inf. Retrieval J. (IF 2.5) Pub Date : 2023-10-31 Marco Markwald, Jiqun Liu, Ran Yu
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DeepQFM: a deep learning based query facets mining method Inf. Retrieval J. (IF 2.5) Pub Date : 2023-10-30 Zhirui Deng, Zhicheng Dou, Ji-Rong Wen
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Learning heterogeneous subgraph representations for team discovery Inf. Retrieval J. (IF 2.5) Pub Date : 2023-10-09 Radin Hamidi Rad, Hoang Nguyen, Feras Al-Obeidat, Ebrahim Bagheri, Mehdi Kargar, Divesh Srivastava, Jaroslaw Szlichta, Fattane Zarrinkalam
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Investigating better context representations for generative question answering Inf. Retrieval J. (IF 2.5) Pub Date : 2023-10-02 Sumam Francis, Marie-Francine Moens
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Multimodal video retrieval with CLIP: a user study Inf. Retrieval J. (IF 2.5) Pub Date : 2023-09-29 Tayfun Alpay, Sven Magg, Philipp Broze, Daniel Speck
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MuMUR: Multilingual Multimodal Universal Retrieval Inf. Retrieval J. (IF 2.5) Pub Date : 2023-09-25 Avinash Madasu, Estelle Aflalo, Gabriela Ben Melech Stan, Shachar Rosenman, Shao-Yen Tseng, Gedas Bertasius, Vasudev Lal
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Temporal information retrieval using bitwise operators Inf. Retrieval J. (IF 2.5) Pub Date : 2023-09-23 Prasanna Koirala, Ramazan Aygun, Tathagata Mukherjee, Haeyong Chung
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FarsNewsQA: a deep learning-based question answering system for the Persian news articles Inf. Retrieval J. (IF 2.5) Pub Date : 2023-03-19 Arefeh Kazemi, Zahra Zojaji, Mahdi Malverdi, Jamshid Mozafari, Fatemeh Ebrahimi, Negin Abadani, Mohammad Reza Varasteh, Mohammad Ali Nematbakhsh
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Shop by image: characterizing visual search in e-commerce Inf. Retrieval J. (IF 2.5) Pub Date : 2023-03-03 Arnon Dagan, Ido Guy, Slava Novgorodov
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An in-depth study on adversarial learning-to-rank Inf. Retrieval J. (IF 2.5) Pub Date : 2023-02-28 Hai-Tao Yu, Rajesh Piryani, Adam Jatowt, Ryo Inagaki, Hideo Joho, Kyoung-Sook Kim
In light of recent advances in adversarial learning, there has been strong and continuing interest in exploring how to perform adversarial learning-to-rank. The previous adversarial ranking methods [e.g., IRGAN by Wang et al. (IRGAN: a minimax game for unifying generative and discriminative information retrieval models. Proceedings of the 40th SIGIR pp. 515–524, 2017)] mainly follow the generative
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Applying burst-tries for error-tolerant prefix search Inf. Retrieval J. (IF 2.5) Pub Date : 2022-10-18 Berg Ferreira, Edleno Silva de Moura, Altigran da Silva
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Sequence-aware news recommendations by combining intra- with inter-session user information Inf. Retrieval J. (IF 2.5) Pub Date : 2022-09-28 Panagiotis Symeonidis, Dmitry Chaltsev, Chemseddine Berbague, Markus Zanker
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Highlighting exact matching via marking strategies for ad hoc document ranking with pretrained contextualized language models Inf. Retrieval J. (IF 2.5) Pub Date : 2022-08-06 Lila Boualili, Jose G. Moreno, Mohand Boughanem
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Reinforcement online learning to rank with unbiased reward shaping Inf. Retrieval J. (IF 2.5) Pub Date : 2022-08-04 Shengyao Zhuang, Zhihao Qiao, Guido Zuccon
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Shallow pooling for sparse labels Inf. Retrieval J. (IF 2.5) Pub Date : 2022-07-20 Negar Arabzadeh, Alexandra Vtyurina, Xinyi Yan, Charles L. A. Clarke
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Exploring latent connections in graph neural networks for session-based recommendation Inf. Retrieval J. (IF 2.5) Pub Date : 2022-07-18 Fei Cai, Zhiqiang Pan, Chengyu Song, Xin Zhang
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Learning user preferences through online conversations via personalized memory transfer Inf. Retrieval J. (IF 2.5) Pub Date : 2022-06-26 Nagaarchana Godavarthy, Yuan Wang, Travis Ebesu, Un Suthee, Min Xie, Yi Fang
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Recommendations for item set completion: on the semantics of item co-occurrence with data sparsity, input size, and input modalities Inf. Retrieval J. (IF 2.5) Pub Date : 2022-04-04 I. Vagliano, L. Galke, A. Scherp
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Guest editorial: special issue on ECIR 2021 Inf. Retrieval J. (IF 2.5) Pub Date : 2022-04-01 Djoerd Hiemstra,Marie-Francine Moens
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CEQE to SQET: A study of contextualized embeddings for query expansion Inf. Retrieval J. (IF 2.5) Pub Date : 2022-03-22 Shahrzad Naseri, Jeffrey Dalton, Andrew Yates, James Allan
In this work, we study recent advances in context-sensitive language models for the task of query expansion. We study the behavior of existing and new approaches for lexical word-based expansion in both unsupervised and supervised contexts. For unsupervised models, we study the behavior of the Contextualized Embeddings for Query Expansion (CEQE) model. We introduce a new model, Supervised Contextualized
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Open-domain conversational search assistants: the Transformer is all you need Inf. Retrieval J. (IF 2.5) Pub Date : 2022-03-14 Rafael Ferreira, Mariana Leite, David Semedo, Joao Magalhaes
On the quest of providing a more natural interaction between users and search systems, open-domain conversational search assistants have emerged, by assisting users in answering questions about open topics in a conversational manner. In this work, we show how the Transformer architecture achieves state-of-the-art results in key IR tasks, leveraging the creation of conversational assistants that engage
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CoSearcher: studying the effectiveness of conversational search refinement and clarification through user simulation Inf. Retrieval J. (IF 2.5) Pub Date : 2022-03-10 Alexandre Salle, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
A key application of conversational search is refining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it infeasible to examine a wide range of user behaviors. To support robust training/evaluation of such systems, we propose a simulation
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sMARE: a new paradigm to evaluate and understand query performance prediction methods Inf. Retrieval J. (IF 2.5) Pub Date : 2022-03-07 Guglielmo Faggioli, Oleg Zendel, J. Shane Culpepper, Nicola Ferro, Falk Scholer
Query performance prediction (QPP) has been studied extensively in the IR community over the last two decades. A by-product of this research is a methodology to evaluate the effectiveness of QPP techniques. In this paper, we re-examine the existing evaluation methodology commonly used for QPP, and propose a new approach. Our key idea is to model QPP performance as a distribution instead of relying
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On cross-lingual retrieval with multilingual text encoders Inf. Retrieval J. (IF 2.5) Pub Date : 2022-03-07 Robert Litschko, Ivan Vulić, Simone Paolo Ponzetto, Goran Glavaš
Pretrained multilingual text encoders based on neural transformer architectures, such as multilingual BERT (mBERT) and XLM, have recently become a default paradigm for cross-lingual transfer of natural language processing models, rendering cross-lingual word embedding spaces (CLWEs) effectively obsolete. In this work we present a systematic empirical study focused on the suitability of the state-of-the-art
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Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation Inf. Retrieval J. (IF 2.5) Pub Date : 2022-01-21 Anirban Chakraborty, Debasis Ganguly, Annalina Caputo, Gareth J. F. Jones
An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized ‘points-of-interest’ (POIs) to a user, if it can extract information from the user’s preference history (exploitation) and effectively blend it with the user’s current contextual information (exploration) to predict a POI’s ‘appropriateness’ in the current context. To balance this trade-off
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Efficient query processing techniques for next-page retrieval Inf. Retrieval J. (IF 2.5) Pub Date : 2022-01-18 Joel Mackenzie, Matthias Petri, Alistair Moffat
In top-k ranked retrieval the goal is to efficiently compute an ordered list of the highest scoring k documents according to some stipulated similarity function such as the well-known BM25 approach. In most implementation techniques a min-heap of size k is used to track the top scoring candidates. In this work we consider the question of how best to retrieve the second page of search results, given
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Measurement of clustering effectiveness for document collections Inf. Retrieval J. (IF 2.5) Pub Date : 2022-01-10 Yuan, Meng, Zobel, Justin, Lin, Pauline
Clustering of the contents of a document corpus is used to create sub-corpora with the intention that they are expected to consist of documents that are related to each other. However, while clustering is used in a variety of ways in document applications such as information retrieval, and a range of methods have been applied to the task, there has been relatively little exploration of how well it
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Search results diversification for effective fair ranking in academic search Inf. Retrieval J. (IF 2.5) Pub Date : 2021-12-07 McDonald, Graham, Macdonald, Craig, Ounis, Iadh
Providing users with relevant search results has been the primary focus of information retrieval research. However, focusing on relevance alone can lead to undesirable side effects. For example, small differences between the relevance scores of documents that are ranked by relevance alone can result in large differences in the exposure that the authors of relevant documents receive, i.e., the likelihood
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Neural ranking models for document retrieval Inf. Retrieval J. (IF 2.5) Pub Date : 2021-10-19 Trabelsi, Mohamed, Chen, Zhiyu, Davison, Brian D., Heflin, Jeff
Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations
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Combining semi-supervised and active learning to rank algorithms: application to Document Retrieval Inf. Retrieval J. (IF 2.5) Pub Date : 2021-10-04 Dammak, Faiza, Kammoun, Hager
Generally, the purpose of learning to rank methods is to combine the results from existing ranking models that within a single ranking function, applied to order the documents as efficiently as possible, improving the quality lists of results returned. However, learning to rank has several limitations namely the creation and size of the labeled database. We have considered the two frameworks of semi-supervised
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Variational Bayesian representation learning for grocery recommendation Inf. Retrieval J. (IF 2.5) Pub Date : 2021-08-27 Meng, Zaiqiao, McCreadie, Richard, Macdonald, Craig, Ounis, Iadh
Representation learning has been widely applied in real-world recommendation systems to capture the features of both users and items. Existing grocery recommendation methods only represent each user and item by single deterministic points in a low-dimensional continuous space, which limit the expressive ability of their embeddings, resulting in recommendation performance bottlenecks. In addition, existing
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Strong natural language query generation Inf. Retrieval J. (IF 2.5) Pub Date : 2021-07-15 Binsheng Liu, Xiaolu Lu, J. Shane Culpepper
In this paper, we propose a novel query generation task we refer to as the Strong Natural Language Query (SNLQ) problem. The key idea we explore is how to best learn document summarization and ranker effectiveness jointly in order to generate human-readable queries which capture the information need conveyed by a document, and that can also be used for refinding tasks and query rewriting. Our problem
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Using word semantic concepts for plagiarism detection in text documents Inf. Retrieval J. (IF 2.5) Pub Date : 2021-07-14 Chia-Yang Chang, Shie-Jue Lee, Chih-Hung Wu, Chih-Feng Liu, Ching-Kuan Liu
Plagiarism is a common problem in the modern age. With the advance of Internet, it is more and more convenient to access other people’s writings or publications. When someone uses the content of a text in an undesirable way, plagiarism may occur. Plagiarism infringes the intellectual property rights, so it is a serious problem nowadays. However, detecting plagiarism effectively is a challenging work
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Pseudo relevance feedback optimization Inf. Retrieval J. (IF 2.5) Pub Date : 2021-05-25 Avi Arampatzis, Georgios Peikos, Symeon Symeonidis
We propose a method for automatic optimization of pseudo relevance feedback (PRF) in information retrieval. Based on the conjecture that the initial query’s contribution to the final query may not be necessary once a good model is built from pseudo relevant documents, we set out to optimize per query only the number of top-retrieved documents to be used for feedback. The optimization is based on several
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Algorithmic copywriting: automated generation of health-related advertisements to improve their performance Inf. Retrieval J. (IF 2.5) Pub Date : 2021-04-13 Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon
Search advertising, a popular method for online marketing, has been employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and experimentation, which may not be available to health authorities wishing to elicit such changes, especially when dealing with public health crises such as epidemic outbreaks. Here, we develop a framework
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Improved reviewer assignment based on both word and semantic features Inf. Retrieval J. (IF 2.5) Pub Date : 2021-04-02 Shicheng Tan, Zhen Duan, Shu Zhao, Jie Chen, Yanping Zhang
Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is a common challenge in the academic community. Current word- and semantic-based approaches treat the reviewer assignment problem (RAP) as an information retrieval problem but do not take into account two constraints of the RAP: incompleteness of the reviewer data and interference from nonmanuscript-related papers.
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Topic-independent modeling of user knowledge in informational search sessions Inf. Retrieval J. (IF 2.5) Pub Date : 2021-03-16 Ran Yu, Rui Tang, Markus Rokicki, Ujwal Gadiraju, Stefan Dietze
Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user’s
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Evaluation metrics for measuring bias in search engine results Inf. Retrieval J. (IF 2.5) Pub Date : 2021-01-27 Gizem Gezici, Aldo Lipani, Yucel Saygin, Emine Yilmaz
Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated
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Guest editorial: special issue on ECIR 2020 Inf. Retrieval J. (IF 2.5) Pub Date : 2021-01-28 Joemon M. Jose,Emine Yilmaz,João Magalhães,Pablo Castells
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Structural textile pattern recognition and processing based on hypergraphs Inf. Retrieval J. (IF 2.5) Pub Date : 2021-01-23 Vuong M. Ngo, Sven Helmer, Nhien-An Le-Khac, M-Tahar Kechadi
The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following
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LSH kNN graph for diffusion on image retrieval Inf. Retrieval J. (IF 2.5) Pub Date : 2021-01-07 Federico Magliani, Andrea Prati
Experimental results demonstrated the goodness of the diffusion mechanism for several computer vision tasks: image retrieval, semi-supervised and supervised learning, image classification. Diffusion requires the construction of a kNN graph in order to work. As predictable, the quality of the created graph influences the final results. Unfortunately, the larger the used dataset is, the more time the
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Improving question answering for event-focused questions in temporal collections of news articles Inf. Retrieval J. (IF 2.5) Pub Date : 2021-01-02 Jiexin Wang, Adam Jatowt, Michael Färber, Masatoshi Yoshikawa
Temporal collections of news articles (or news archives) contain numerous accurate and time-aligned articles, which offer immense value to our society, helping users to know details of events that occurred at specific time points in the past. Currently, the access to such collections is rather difficult for average users due to their large sizes and complexities. For better use of these valuable resources
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Review helpfulness evaluation and recommendation based on an attention model of customer expectation Inf. Retrieval J. (IF 2.5) Pub Date : 2021-01-02 Xianshan Qu, Xiaopeng Li, Csilla Farkas, John Rose
With the fast growth of e-commerce, more people choose to purchase products online and browse reviews before making decisions. It is essential to identify helpful reviews, given the typical large number of reviews and the various range of quality. In this paper, we aim to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer’s expectation
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A comparison of automatic Boolean query formulation for systematic reviews Inf. Retrieval J. (IF 2.5) Pub Date : 2020-10-27 Harrisen Scells, Guido Zuccon, Bevan Koopman
Systematic reviews are comprehensive literature reviews that target a highly focused research question. In the medical domain, complex Boolean queries are used to identify studies. To ensure comprehensiveness, all studies retrieved are screened for inclusion or exclusion in the review. Developing Boolean queries for this task requires the expertise of trained information specialists. However, even
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Special issue on learning from user interactions Inf. Retrieval J. (IF 2.5) Pub Date : 2020-10-24 Rishabh Mehrotra,Ahmed Hassan Awadallah,Emine Yilmaz
When users interact with online services (e.g. search engines, recommender systems, conversational agents), they leave behind traces of interaction patterns. The ability to record and interpret user interaction signals (Guo and Agichtein 2012) and understand user behavior (Mehrotra et al. 2016a) gives online systems a vast treasure trove of insights for improvement and experimentation. More generally
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Foreword to the special issue on mining actionable insights from online user generated content Inf. Retrieval J. (IF 2.5) Pub Date : 2020-07-31 Marcelo G. Armentano,Ebrahim Bagheri,Julia Kiseleva,Frank W. Takes
In recent years, the dissemination and use of online social networking platforms have grown significantly. Modern social networks have billions of users that constantly share information with each other. The enormity and high variance of the information that propagates through these social networks influence the public discourse in topics that range from marketing, education, business, and healthcare
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Robust keyword search in large attributed graphs Inf. Retrieval J. (IF 2.5) Pub Date : 2020-07-22 Spencer Bryson, Heidar Davoudi, Lukasz Golab, Mehdi Kargar, Yuliya Lytvyn, Piotr Mierzejewski, Jaroslaw Szlichta, Morteza Zihayat
There is a growing need to explore attributed graphs such as social networks, expert networks, and biological networks. A well-known mechanism for non-technical users to explore such graphs is keyword search, which receives a set of query keywords and returns a connected subgraph that contains the keywords. However, existing approaches, such as methods based on shortest paths between nodes containing
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Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering Inf. Retrieval J. (IF 2.5) Pub Date : 2020-06-19 Zhipeng Zhang, Yao Zhang, Yonggong Ren
Recommender system (RS) can produce personalized service to users by analyzing their historical information. User-based collaborative filtering (UBCF) approach is widely utilized in practical RSs because of its excellent performance. However, the traditional UBCF suffers from several inherent problems, such as data sparsity and new user cold start. In this paper, we propose a novel approach, namely
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Assessing ranking metrics in top-N recommendation Inf. Retrieval J. (IF 2.5) Pub Date : 2020-06-08 Daniel Valcarce, Alejandro Bellogín, Javier Parapar, Pablo Castells
The evaluation of recommender systems is an area with unsolved questions at several levels. Choosing the appropriate evaluation metric is one of such important issues. Ranking accuracy is generally identified as a prerequisite for recommendation to be useful. Ranking metrics have been adapted for this purpose from the Information Retrieval field into the recommendation task. In this article, we undertake
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On the foundations of similarity in information access Inf. Retrieval J. (IF 2.5) Pub Date : 2020-06-02 Enrique Amigó, Fernando Giner, Julio Gonzalo, Felisa Verdejo
Although computing similarity is one of the fundamental challenges of Information Access tasks, the notion of similarity in this context is not yet completely understood from a formal, axiomatic perspective. In this paper we show how axiomatic explanations of similarity from other fields (Tversky’s axioms from the point of view of cognitive sciences, and metric spaces from the point of view of algebra)
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On the nature of information access evaluation metrics: a unifying framework Inf. Retrieval J. (IF 2.5) Pub Date : 2020-05-29 Enrique Amigó, Stefano Mizzaro
We provide a uniform, general, and complete formal account of evaluation metrics for ranking, classification, clustering, and other information access problems. We leverage concepts from measurement theory, such as scale types and permissible transformation functions, and we capture the nature of evaluation metrics in many tasks by two formal definitions, which lead to a distinction of two metric/tasks
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Axiomatic thinking for information retrieval: introduction to special issue Inf. Retrieval J. (IF 2.5) Pub Date : 2020-05-25 Enrique Amigó,Hui Fang,Stefano Mizzaro,Chengxiang Zhai
The dominant methodology of Information Retrieval (IR) research has so far been empirical, i.e., progress is guided by experimental results on various data sets. The availability of large data sets since the 90s, the growing computational power, and the possibility of automatizing experiments have accelerated empirical studies of IR in the last two decades, generating many empirical findings. However
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Sharing emotions: determining films’ evoked emotional experience from their online reviews Inf. Retrieval J. (IF 2.5) Pub Date : 2020-05-09 Osnat Mokryn, David Bodoff, Nadim Bader, Yael Albo, Joel Lanir
Online reviews are broadly believed to reflect consumers’ opinions towards the reviewed items. In this work, we postulate that online reviews for experience goods also reflect something very different, the reviewer’s emotions while experiencing the item. We study the case of films, which are made with the intent of evoking an emotional response. We postulate that the emotions that the viewer experienced
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Preference-based interactive multi-document summarisation Inf. Retrieval J. (IF 2.5) Pub Date : 2019-11-19 Yang Gao, Christian M. Meyer, Iryna Gurevych
Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound. Preference-based interactive learning has been successfully applied, but the existing methods require several thousand interaction rounds even in simulations with perfect user feedback. In this paper, we study preference-based interactive summarisation. To reduce the number of interaction
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Boosting learning to rank with user dynamics and continuation methods Inf. Retrieval J. (IF 2.5) Pub Date : 2019-11-05 Nicola Ferro, Claudio Lucchese, Maria Maistro, Raffaele Perego
Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction