A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0

Serrano-Guerrero, Jesús; Liu, Xinyue; Olivas, José Á.; Cerezo, Andres; Romero, Francisco P.

doi:10.1016/j.ins.2011.01.012

Cited by 133 publications

(43 citation statements)

References 51 publications

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“…In this direction, some early works enriched the typical content-based recommendation scheme by incorporating fuzzy linguistic variables for modelling the preferences [79], subsequently considering more flexible frameworks for capturing the uncertainty of such user's preferences [80], even considering incomplete information [84]. Additionally, such sophisticated fuzzy linguistic approaches have also supported the construction of systems for recommending diverse items such as research resources [100,115], or furniture products [42].…”

Section: Proposalsmentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

149

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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Section: Proposalsmentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

149

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“…Example 3.2 In many social surveys, respondents are customarily asked by means of a questionnaire to indicate their choices from a set of prefixed Likerttype items. Many researchers consider Likert-type labels as fuzzy numbervalued ones, by identifying the generic response to a question with a fuzzy linguistic variable (see, for instance, Serrano-Guerrero et al [21], Porcel et al [18], for recent studies about). As indicated by Chou [7], "often the wording of response levels clearly implies a symmetry of response levels about a middle category; at the very least, such an item would fall between ordinal-level and interval-level measurement...…”

Section: Symmetric Random Fuzzy Numbersmentioning

confidence: 99%

Central tendency for symmetric random fuzzy numbers

Sinova

Casals

Alvarez

2014

Information Sciences

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Random fuzzy numbers are becoming a valuable tool to model and handle fuzzyvalued data generated through a random process. Recent studies have been devoted to introduce measures of the central tendency of random fuzzy numbers showing a more robust behaviour than the so-called Aumann-type mean value. This paper aims to deepen in the (rather comparative) analysis of these centrality measures and the Aumann-type mean by examining the situation of symmetric random fuzzy numbers. Similarities and differences with the real-valued case are pointed out and theoretical conclusions are accompanied with some illustrative examples.

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“…The F1 Measure [18] combines the precision and recall into a single metric and has been used in many research projects, e.g. [46,45,49]. F1 is computed as follows:…”

Section: Approachmentioning

confidence: 99%

Kernel-Mapping Recommender system algorithms

Ghazanfar

Prügel-Bennett

Szedmák

2012

Information Sciences

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Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. In this paper, we propose a new algorithm that we call the Kernel-Mapping Recommender (KMR), which uses a novel structure learning technique. This paper makes the following contributions: we show how (1) user-based and item-based versions of the KMR algorithm can be built; (2) user-based and item-based versions can be combined; (3) more informationfeatures, genre, etc.-can be employed using kernels and how this affects the final results; and (4) to make reliable recommendations under sparse, cold-start, and long tail scenarios. By extensive experimental results on five different datasets, we show that the proposed algorithms outperform or give comparable results to other state-of-the-art algorithms.

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A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0

Cited by 133 publications

References 51 publications

Fuzzy Tools in Recommender Systems: A Survey

Fuzzy Tools in Recommender Systems: A Survey

Central tendency for symmetric random fuzzy numbers

Kernel-Mapping Recommender system algorithms

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