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  • 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

    更新日期:2020-06-19
  • 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

    更新日期:2020-06-08
  • 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)

    更新日期:2020-06-02
  • 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

    更新日期:2020-05-29
  • 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

    更新日期:2020-05-09
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    更新日期:2020-04-21
  • 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

    Abstract 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

    更新日期:2020-03-07
  • 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

    更新日期:2019-09-21
  • 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

    更新日期:2019-09-04
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