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  • LSH kNN graph for diffusion on image retrieval
    Inf. Retrieval J. (IF 2.209) 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

  • Improving question answering for event-focused questions in temporal collections of news articles
    Inf. Retrieval J. (IF 2.209) 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

  • Review helpfulness evaluation and recommendation based on an attention model of customer expectation
    Inf. Retrieval J. (IF 2.209) 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

  • A comparison of automatic Boolean query formulation for systematic reviews
    Inf. Retrieval J. (IF 2.209) 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

  • Robust keyword search in large attributed graphs
    Inf. Retrieval J. (IF 2.209) 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

  • Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering
    Inf. Retrieval J. (IF 2.209) 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

  • Assessing ranking metrics in top-N recommendation
    Inf. Retrieval J. (IF 2.209) 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

  • On the foundations of similarity in information access
    Inf. Retrieval J. (IF 2.209) 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)

  • On the nature of information access evaluation metrics: a unifying framework
    Inf. Retrieval J. (IF 2.209) 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

  • Sharing emotions: determining films’ evoked emotional experience from their online reviews
    Inf. Retrieval J. (IF 2.209) 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

  • Preference-based interactive multi-document summarisation
    Inf. Retrieval J. (IF 2.209) 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

  • Boosting learning to rank with user dynamics and continuation methods
    Inf. Retrieval J. (IF 2.209) 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

  • An axiomatic approach to corpus-based cross-language information retrieval
    Inf. Retrieval J. (IF 2.209) Pub Date : 2020-04-09
    Razieh Rahimi; Ali Montazeralghaem; Azadeh Shakery

    A major challenge in cross-language information retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects term weighting which is known to highly impact the retrieval performance. Despite its importance, how different approaches for integration of translation knowledge into retrieval models relatively perform has not been analytically examined. In this paper, we present

  • Offline evaluation options for recommender systems
    Inf. Retrieval J. (IF 2.209) Pub Date : 2020-03-18
    Rocío Cañamares; Pablo Castells; Alistair Moffat

    We undertake a detailed examination of the steps that make up offline experiments for recommender system evaluation, including the manner in which the available ratings are filtered and split into training and test; the selection of a subset of the available users for the evaluation; the choice of strategy to handle the background effects that arise when the system is unable to provide scores for some

  • A passage-based approach to learning to rank documents
    Inf. Retrieval J. (IF 2.209) Pub Date : 2020-03-06
    Eilon Sheetrit; Anna Shtok; Oren Kurland

    According to common relevance-judgments regimes, such as TREC’s, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document’s passages. However, the main source of passage-based information utilized was passage-query

  • Informational, transactional, and navigational need of information: relevance of search intention in search engine advertising
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-11-26
    Carsten D. Schultz

    This study investigates the impact of search query intention when evaluating and managing search engine advertising. Specifically, we study whether the performance of a search engine advertising campaign depends on the informational, transactional, and navigational search intentions and also consider the appearance of an organic result alongside a search engine advertisement on the same search engine

  • ReBoost: a retrieval-boosted sequence-to-sequence model for neural response generation
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-09-23
    Yutao Zhu; Zhicheng Dou; Jian-Yun Nie; Ji-Rong Wen

    Human–computer conversation is an active research topic in natural language processing. One of the representative methods to build conversation systems uses the sequence-to-sequence (Seq2seq) model through neural networks. However, with limited input information, the Seq2seq model tends to generate meaningless and trivial responses. It can be greatly enhanced if more supplementary information is provided

  • Evaluation measures for quantification: an axiomatic approach
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-09-21
    Fabrizio Sebastiani

    Quantification is the task of estimating, given a set \(\sigma \) of unlabelled items and a set of classes \({\mathcal {C}}=\{c_{1}, \ldots , c_{|{\mathcal {C}}|}\}\), the prevalence (or “relative frequency”) in \(\sigma \) of each class \(c_{i}\in {\mathcal {C}}\). While quantification may in principle be solved by classifying each item in \(\sigma \) and counting how many such items have been labelled

  • How do interval scales help us with better understanding IR evaluation measures?
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-09-04
    Marco Ferrante; Nicola Ferro; Eleonora Losiouk

    Evaluation measures are the basis for quantifying the performance of IR systems and the way in which their values can be processed to perform statistical analyses depends on the scales on which these measures are defined. For example, mean and variance should be computed only when relying on interval scales. In our previous work we defined a theory of IR evaluation measures, based on the representational

  • Evaluating sentence-level relevance feedback for high-recall information retrieval
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-08-13
    Haotian Zhang; Gordon V. Cormack; Maura R. Grossman; Mark D. Smucker

    This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback. Under the weak assumption that more time and effort is required to review an entire document than a single sentence, simulation results indicate

  • Deep cross-platform product matching in e-commerce
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-08-13
    Juan Li; Zhicheng Dou; Yutao Zhu; Xiaochen Zuo; Ji-Rong Wen

    Online shopping has become more and more popular in recent years, which leads to a prosperity on online platforms. Generally, the identical products are provided by many sellers on multiple platforms. Thus the comparison between products on multiple platforms becomes a basic demand for both consumers and sellers. However, identifying identical products on multiple platforms is difficult because the

  • Abstraction of query auto completion logs for anonymity-preserving analysis
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-06-06
    Unni Krishnan; Bodo Billerbeck; Alistair Moffat; Justin Zobel

    Query auto completion (QAC) is used in search interfaces to interactively offer a list of suggestions to users as they enter queries. The suggested completions are updated each time the user modifies their partial query, as they either add further keystrokes or interact directly with completions that have been offered. In this work we use a state model to capture the possible interactions that can

  • The impact of result diversification on search behaviour and performance
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-05-16
    David Maxwell; Leif Azzopardi; Yashar Moshfeghi

    Result diversification aims to provide searchers with a broader view of a given topic while attempting to maximise the chances of retrieving relevant material. Diversifying results also aims to reduce search bias by increasing the coverage over different aspects of the topic. As such, searchers should learn more about the given topic in general. Despite diversification algorithms being introduced over

  • Fewer topics? A million topics? Both?! On topics subsets in test collections
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-05-08
    Kevin Roitero; J. Shane Culpepper; Mark Sanderson; Falk Scholer; Stefano Mizzaro

    When evaluating IR run effectiveness using a test collection, a key question is: What search topics should be used? We explore what happens to measurement accuracy when the number of topics in a test collection is reduced, using the Million Query 2007, TeraByte 2006, and Robust 2004 TREC collections, which all feature more than 50 topics, something that has not been examined in past work. Our analysis

  • Low-cost, bottom-up measures for evaluating search result diversification
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-04-20
    Zhicheng Dou; Xue Yang; Diya Li; Ji-Rong Wen; Tetsuya Sakai

    Search result diversification aims at covering different user intents by returning a diversified document list. Most existing diversity measures require a predefined set of intents for a given query, where it is assumed that there is no relationship across these intents. However, studies have shown that modeling a hierarchy of intents has some benefits over the standard measure of using a flat list

  • A comparison of filtering evaluation metrics based on formal constraints
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-04-01
    Enrique Amigó; Julio Gonzalo; Felisa Verdejo; Damiano Spina

    Although document filtering is simple to define, there is a wide range of different evaluation measures that have been proposed in the literature, all of which have been subject to criticism. Our goal is to compare metrics from a formal point of view, in order to understand whether each metric is appropriate, why and when, in order to achieve a better understanding of the similarities and differences

  • Optimizing the recency-relevance-diversity trade-offs in non-personalized news recommendations
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-02-27
    Abhijnan Chakraborty; Saptarshi Ghosh; Niloy Ganguly; Krishna P. Gummadi

    Online news media sites are emerging as the primary source of news for a large number of users. Due to a large number of stories being published in these media sites, users usually rely on news recommendation systems to find important news. In this work, we focus on automatically recommending news stories to all users of such media websites, where the selection is not influenced by a particular user’s

  • On the impact of group size on collaborative search effectiveness
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-01-09
    Felipe Moraes; Kilian Grashoff; Claudia Hauff

    While today’s web search engines are designed for single-user search, over the years research efforts have shown that complex information needs—which are explorative, open-ended and multi-faceted—can be answered more efficiently and effectively when searching in collaboration. Collaborative search (and sensemaking) research has investigated techniques, algorithms and interface affordances to gain insights

  • Neural architecture for question answering using a knowledge graph and web corpus
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-01-07
    Uma Sawant; Saurabh Garg; Soumen Chakrabarti; Ganesh Ramakrishnan

    In Web search, entity-seeking queries often trigger a special question answering (QA) system. It may use a parser to interpret the question to a structured query, execute that on a knowledge graph (KG), and return direct entity responses. QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the response. Moreover, KG coverage is patchy. At the other

  • Demographic differences in search engine use with implications for cohort selection
    Inf. Retrieval J. (IF 2.209) Pub Date : 2019-01-01
    Elad Yom-Tov

    The correlation between the demographics of users and the text they write has been investigated through literary texts and, more recently, social media. However, differences pertaining to language use in search engines has not been thoroughly analyzed, especially for age and gender differences. Such differences are important especially due to the growing use of search engine data in the study of human

  • Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-12-17
    Guruprasad Nayak; Sourav Dutta; Deepak Ajwani; Patrick Nicholson; Alessandra Sala

    Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of concepts. As a knowledge hierarchy evolves with these automated techniques, there is a need for measures to assess its temporal evolution, quantifying the similarities between different

  • A selective approach to index term weighting for robust information retrieval based on the frequency distributions of query terms
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-12-13
    Ahmet Arslan; Bekir Taner Dinçer

    A typical information retrieval (IR) system applies a single retrieval strategy to every information need of users. However, the results of the past IR experiments show that a particular retrieval strategy is in general good at fulfilling some type of information needs while failing to fulfil some other type, i.e., high variation in retrieval effectiveness across information needs. On the other hand

  • Identifying and exploiting target entity type information for ad hoc entity retrieval
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-12-05
    Darío Garigliotti; Faegheh Hasibi; Krisztian Balog

    Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval

  • Payoffs and pitfalls in using knowledge-bases for consumer health search
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-11-08
    Jimmy; Guido Zuccon; Bevan Koopman

    Consumer health search (CHS) is a challenging domain with vocabulary mismatch and considerable domain expertise hampering peoples’ ability to formulate effective queries. We posit that using knowledge bases for query reformulation may help alleviate this problem. How to exploit knowledge bases for effective CHS is nontrivial, involving a swathe of key choices and design decisions (many of which are

  • Neural variational entity set expansion for automatically populated knowledge graphs
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-10-25
    Pushpendre Rastogi; Adam Poliak; Vince Lyzinski; Benjamin Van Durme

    We propose Neural variational set expansion to extract actionable information from a noisy knowledge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity set expansion task based on a realistic automatically generated KG.

  • Overcoming low-utility facets for complex answer retrieval
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-10-24
    Sean MacAvaney; Andrew Yates; Arman Cohan; Luca Soldaini; Kai Hui; Nazli Goharian; Ophir Frieder

    Many questions cannot be answered simply; their answers must include numerous nuanced details and context. Complex Answer Retrieval (CAR) is the retrieval of answers to such questions. These questions can be constructed from a topic entity (e.g., ‘cheese’) and a facet (e.g., ‘health effects’). While topic matching has been thoroughly explored, we observe that some facets use general language that is

  • Search bias quantification: investigating political bias in social media and web search
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-08-21
    Juhi Kulshrestha; Motahhare Eslami; Johnnatan Messias; Muhammad Bilal Zafar; Saptarshi Ghosh; Krishna P. Gummadi; Karrie Karahalios

    Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the

  • Those were the days: learning to rank social media posts for reminiscence
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-08-11
    Kaweh Djafari Naini; Ricardo Kawase; Nattiya Kanhabua; Claudia Niederée; Ismail Sengor Altingovde

    Social media posts are a great source for life summaries aggregating activities, events, interactions and thoughts of the last months or years. They can be used for personal reminiscence as well as for keeping track with developments in the lives of not-so-close friends. One of the core challenges of automatically creating such summaries is to decide which posts are memorable, i.e., should be considered

  • Beyond word embeddings: learning entity and concept representations from large scale knowledge bases
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-08-11
    Walid Shalaby; Wlodek Zadrozny; Hongxia Jin

    Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts

  • User interest prediction over future unobserved topics on social networks
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-07-10
    Fattane Zarrinkalam; Mohsen Kahani; Ebrahim Bagheri

    The accurate prediction of users’ future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current

  • Predicting trading interactions in an online marketplace through location-based and online social networks
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-07-09
    Lukas Eberhard; Christoph Trattner; Martin Atzmueller

    Link prediction is a prominent research direction e.g., for inferring upcoming interactions to be used in recommender systems. Although this problem of predicting links between users has been extensively studied in the past, research investigating this issue simultaneously in multiplex networks is rather rare so far. This is the focus of this paper. We investigate the extent to which trading interactions

  • Influence me! Predicting links to influential users
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-07-06
    Ariel Monteserin; Marcelo G. Armentano

    In addition to being in contact with friends, online social networks are commonly used as a source of information, suggestions and recommendations from members of the community. Whenever we accept a suggestion or perform any action because it was recommended by a “friend”, we are being influenced by him/her. For this reason, it is useful for users seeking for interesting information to identify and

  • Determining the interests of social media users: two approaches
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-07-05
    Nacéra Bennacer Seghouani; Coriane Nana Jipmo; Gianluca Quercini

    Although social media platforms serve diverse purposes, from social and professional networking to photo sharing and blogging, people frequently use them to share the thoughts and opinions and most importantly, their interests (e.g., politics, economy, sports). Understanding the interests of social media users is key to many applications that need to characterize them to recommend some services and

  • A systematic approach to normalization in probabilistic models
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-06-30
    Aldo Lipani; Thomas Roelleke; Mihai Lupu; Allan Hanbury

    Every information retrieval (IR) model embeds in its scoring function a form of term frequency (TF) quantification. The contribution of the term frequency is determined by the properties of the function of the chosen TF quantification, and by its TF normalization. The first defines how independent the occurrences of multiple terms are, while the second acts on mitigating the a priori probability of

  • A topic recommender for journalists
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-06-14
    Alessandro Cucchiarelli; Christian Morbidoni; Giovanni Stilo; Paola Velardi

    The way in which people gather information about events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources has become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen

  • ( CF ) 2 architecture: contextual collaborative filtering
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-05-16
    Dennis Bachmann; Katarina Grolinger; Hany ElYamany; Wilson Higashino; Miriam Capretz; Majid Fekri; Bala Gopalakrishnan

    Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing

  • An analysis of evaluation campaigns in ad-hoc medical information retrieval: CLEF eHealth 2013 and 2014
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-05-03
    Lorraine Goeuriot; Gareth J. F. Jones; Liadh Kelly; Johannes Leveling; Mihai Lupu; Joao Palotti; Guido Zuccon

    Since its inception in 2013, one of the key contributions of the CLEF eHealth evaluation campaign has been the organization of an ad-hoc information retrieval (IR) benchmarking task. This IR task evaluates systems intended to support laypeople searching for and understanding health information. Each year the task provides registered participants with standard IR test collections consisting of a document

  • Linear feature extraction for ranking
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-05-02
    Gaurav Pandey; Zhaochun Ren; Shuaiqiang Wang; Jari Veijalainen; Maarten de Rijke

    We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix

  • A non-parametric topical relevance model
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-04-04
    Debasis Ganguly; Gareth J. F. Jones

    An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user’s query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search. Standard PRF approaches utilize the information contained

  • A language model-based framework for multi-publisher content-based recommender systems
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-02-06
    Hamed Zamani; Azadeh Shakery

    The rapid growth of the Web has increased the difficulty of finding the information that can address the users’ information needs. A number of recommendation approaches have been developed to tackle this problem. The increase in the number of data providers has necessitated the development of multi-publisher recommender systems; systems that include more than one item/data provider. In such environments

  • An artist ranking system based on social media mining
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-02-05
    Amalia F. Foka

    Currently users on social media post their opinion and feelings about almost everything. This online behavior has led to numerous applications where social media data are used to measure public opinion in a similar way as a poll or a survey. In this paper, we will present an application of social media mining for the art market. To the best of our knowledge, this will be the first attempt to mine social

  • Hybrid query expansion model for text and microblog information retrieval
    Inf. Retrieval J. (IF 2.209) Pub Date : 2018-02-03
    Meriem Amina Zingla; Chiraz Latiri; Philippe Mulhem; Catherine Berrut; Yahya Slimani

    Query expansion (QE) is an important process in information retrieval applications that improves the user query and helps in retrieving relevant results. In this paper, we introduce a hybrid query expansion model (HQE) that investigates how external resources can be combined to association rules mining and used to enhance expansion terms generation and selection. The HQE model can be processed in different

  • EveTAR : building a large-scale multi-task test collection over Arabic tweets
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-12-21
    Maram Hasanain; Reem Suwaileh; Tamer Elsayed; Mucahid Kutlu; Hind Almerekhi

    This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent

  • Clustering small-sized collections of short texts
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-11-30
    Lili Kotlerman; Ido Dagan; Oren Kurland

    The need to cluster small text corpora composed of a few hundreds of short texts rises in various applications; e.g., clustering top-retrieved documents based on their snippets. This clustering task is challenging due to the vocabulary mismatch between short texts and the insufficient corpus-based statistics (e.g., term co-occurrence statistics) due to the corpus size. We address this clustering challenge

  • Website replica detection with distant supervision
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-11-29
    Cristiano Carvalho; Edleno Silva de Moura; Adriano Veloso; Nivio Ziviani

    Duplicate content on the Web occurs within the same website or across multiple websites. The latter is mainly associated with the existence of website replicas—sites that are perceptibly similar. Replication may be accidental, intentional or malicious, but no matter the reason, search engines suffer greatly either from unnecessarily storing and moving duplicate data, or from providing search results

  • Neural information retrieval: at the end of the early years
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-11-10
    Kezban Dilek Onal; Ye Zhang; Ismail Sengor Altingovde; Md Mustafizur Rahman; Pinar Karagoz; Alex Braylan; Brandon Dang; Heng-Lu Chang; Henna Kim; Quinten McNamara; Aaron Angert; Edward Banner; Vivek Khetan; Tyler McDonnell; An Thanh Nguyen; Dan Xu; Byron C. Wallace; Maarten de Rijke; Matthew Lease

    A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into

  • Using word embeddings in Twitter election classification
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-11-09
    Xiao Yang; Craig Macdonald; Iadh Ounis

    Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to

  • A study of untrained models for multimodal information retrieval
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-11-03
    Melanie Imhof; Martin Braschler

    Operational multimodal information retrieval systems have to deal with increasingly complex document collections and queries that are composed of a large set of textual and non-textual modalities such as ratings, prices, timestamps, geographical coordinates, etc. The resulting combinatorial explosion of modality combinations makes it intractable to treat each modality individually and to obtain suitable

  • Picture it in your mind: generating high level visual representations from textual descriptions
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-10-14
    Fabio Carrara; Andrea Esuli; Tiziano Fagni; Fabrizio Falchi; Alejandro Moreo Fernández

    In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation. Searching in the visual feature space has the advantage that any update to the translation model

  • Sequence-based context-aware music recommendation
    Inf. Retrieval J. (IF 2.209) Pub Date : 2017-10-13
    Dongjing Wang; Shuiguang Deng; Guandong Xu

    Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements

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