A Regularity-Based Preprocessing Method for Collaborative Recommender Systems

Toledo, Raciel Yera; Mota, Yailé Caballero; Borroto, Milton García

doi:10.3745/jips.2013.9.3.435

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Instead, it is composed by a term (called regularity term), that represents a possible rating value of the current user over certain item. As far as we know, we only identified the use of the concept of regularity in RSs in the research work developed by Yera et al [32], where the authors presented some evidences that regularities could be used for performing data preprocessing in RSs. However, such work only presents an initial analysis and does not consider any kind of uncertainty management.…”

Section: Formalizing Rating Regularitiesmentioning

confidence: 99%

Exploring Fuzzy Rating Regularities for Managing Natural Noise in Collaborative Recommendation

Toledo¹,

Barranco²,

Alzahrani³

et al. 2019

IJCIS

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Recommender systems have played a relevant role in e-commerce for supporting online users to obtain suggestions about products that best fit their preferences and needs in overloaded search spaces. In such a context, several authors have proposed methods focused on removing the users' inconsistencies when they rate items, so-called natural noise, improving in this way the recommendation performance. The current paper explores the use of rating regularities for managing the natural noise in collaborative filtering recommendation, having as key feature the use of fuzzy techniques for coping with the uncertainty associated to such scenarios. Specifically, such regularities are used for representing common rating patterns and thus detect noisy ratings when they tend to contradict such patterns. An experimental study is developed for showing the performance of the proposal, as well as analyzing its behavior in contrast to previous natural noise management procedures.

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Section: Formalizing Rating Regularitiesmentioning

confidence: 99%

Exploring Fuzzy Rating Regularities for Managing Natural Noise in Collaborative Recommendation

Toledo¹,

Barranco²,

Alzahrani³

et al. 2019

IJCIS

View full text Add to dashboard Cite

show abstract

“…These methods filter or evaluate items through the opinions of other users [20] [14]. They are usually based on the assumption that the given user will prefer the items which other users with similar preferences liked in the past [1,13,24]. The collaboration filtering algorithms can be divided into two categories: memory-based algorithms and model based algorithms.…”

Section: Recommendation Systemmentioning

confidence: 99%

Recommendation system for sharing economy based on multidimensional trust model

Kim

Yoon

2014

Multimed Tools Appl

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The recommendation system are widely adopted in today's mainstream online sharing services, providing useful prediction of user's rating or user's preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user's decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user's behavior based on rating analysis and identification of users' historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users' needs, are the focus of this paper.

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“…As a result, systems that recommend content and products that are likely to be preferred by users are widely employed, and various personalized services utilizing such recommendation systems have been provided [18,19,20]. Therefore, studies on recommendation schemes considering users' preferences have been conducted actively to take various user requirements into account quickly [9,10,18,21,22].…”

Section: Introductionmentioning

confidence: 99%