Methods of recommender system: A review

Patel, Bansari; Desai, Palak V.; Panchal, Urvi

doi:10.1109/iciiecs.2017.8275856

Cited by 50 publications

(17 citation statements)

References 10 publications

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“…Sometimes people want to try something new, which is different from their past behavior. The changing of user preferences is one of the main challenges faced by online recommendation services ( Patel, Desai, & Panchal, 2017 ). Thus, personalization may be perceived as the least important driver thus far.…”

Section: Discussionmentioning

confidence: 99%

An empirical investigation of the utilitarian, social benefits in LBS information disclosure—The moderating effect of the gender based social role theory

Mou

et al. 2021

International Journal of Information Management

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Highlights Locatability, personalization and social benefit affect information disclosure intention. Locatability shows the greatest impact on information disclosure intention in LBS. Locatability and personalization are more important for female users in predicting information disclosure intention. Social benefit has a more salient effect on information disclosure intention for male users than female users.

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Section: Discussionmentioning

confidence: 99%

An empirical investigation of the utilitarian, social benefits in LBS information disclosure—The moderating effect of the gender based social role theory

Mou

et al. 2021

International Journal of Information Management

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“…This not only impedes the diversity of experiences of users, but also causes filter bubbles [12,13], e.g., a reduced spectrum of user consumption and the political bias, which limit discovery or neglect the potential for promoting new items from the long tail [14,15]. Moreover, as the training of a recommender system mostly relies on explicit feedback (typically the ratings of items), it is inevitable to face the fact that only a small portion of users leave their ratings-the so-called the sparsity problem [16,17].…”

Section: Introductionmentioning

confidence: 99%

Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

Ban

Lee

2021

Applied Sciences

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Many studies have been conducted on recommender systems in both the academic and industrial fields, as they are currently broadly used in various digital platforms to make personalized suggestions. Despite the improvement in the accuracy of recommenders, the diversity of interest areas recommended to a user tends to be reduced, and the sparsity of explicit feedback from users has been an important issue for making progress in recommender systems. In this paper, we introduce a novel approach, namely re-enrichment learning, which effectively leverages the implicit logged feedback from users to enhance user retention in a platform by enriching their interest areas. The approach consists of (i) graph-based domain transfer and (ii) metadata saliency, which (i) find an adaptive and collaborative domain representing the relations among many users’ metadata and (ii) extract attentional features from a user’s implicit logged feedback, respectively. The experimental results show that our proposed approach has a better capacity to enrich the diversity of interests of a user by means of implicit feedback and to help recommender systems achieve more balanced personalization. Our approach, finally, helps recommenders improve user retention, i.e., encouraging users to click more items or dwell longer on the platform.

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“…En un inicio los Sistemas de Recomendación emergen como unaárea de investigación individual cuando algunos investigadores iniciaron a trabajar en diferentes problemas de recomendación [23].…”

Section: Introductionunclassified

Análsis, diseño y desarrollo de un sistema de recomendación basado en datos restauranteros de TripAdvisor y Foursquare

Perez¹,

Cuecuecha²,

Ramírez³

et al. 2018

RCS

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Methods of recommender system: A review

Cited by 50 publications

References 10 publications

An empirical investigation of the utilitarian, social benefits in LBS information disclosure—The moderating effect of the gender based social role theory

An empirical investigation of the utilitarian, social benefits in LBS information disclosure—The moderating effect of the gender based social role theory

Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

Análsis, diseño y desarrollo de un sistema de recomendación basado en datos restauranteros de TripAdvisor y Foursquare

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