A trust-semantic fusion-based recommendation approach for e-business applications

Shambour, Qusai; Lu, Jie

doi:10.1016/j.dss.2012.09.005

Cited by 152 publications

(78 citation statements)

References 22 publications

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“…Both academia and industry have put in much effort to design efficient algorithms and improve the performance of recommendation systems. There are three main streams of recommendation systems: content-based recommendation systems, collaborative filtering recommendation systems and hybrid recommendation systems [13,17,28,33]. Content-based recommendation systems recommend items that are similar to user's previous preference.…”

Section: Recommendation Systemsmentioning

confidence: 99%

Connecting Researchers with Companies for University-Industry Collaboration

Wang

Ma²,

Liao

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

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Section: Recommendation Systemsmentioning

confidence: 99%

Connecting Researchers with Companies for University-Industry Collaboration

Wang

Ma²,

Liao

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…For the neighbor selection process, the top-N and best neighborhood is selected for active user based on biological metaphor of ant colonies to generate recommendations. Computing the rating prediction is the final step in the recommendation process, we use the deviation-from-mean approach [8] to calculate the predicted rating value the node user u on item i, …”

Section: The Proposed Itars Approachmentioning

confidence: 99%

An Improved Trust-aware Recommender System for Personalized User Recommendation in Tmall

Cheng¹,

Fan²,

Yu³

et al. 2016

dtetr

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Abstract. Annual Double Eleven shopping festival has attracted people's attention greatly. With the increase of online shopping channel, trust has become the most important content of user interaction in this environment. In this paper, We proposed improved trust-aware recommender system (iTARS) produces valuable recommendations by dynamic trust between users and selecting a best neighborhood based on biological metaphor of ant colonies in Tmall. The performance of iTARS is evaluated using tmall datasets of differernt sparsity levels and compared with traditional trust-aware recommender system for generating recommendations to the tmall members.

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“…Cold start problem appears due to the existence of new users or items that not received any ratings [1]. Furthermore, if the number of rating on the existing items is very small, the sparsity problem occurs.…”

Section: Introductionmentioning

confidence: 99%

Trust aware recommender system with distrust in different views of trusted users

Mahtar

Masrom

Nouali

et al. 2018

J. Fundam and Appl Sci.

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A recommender system aims to provide users with personalized online product or service recommendations to handle the online information overload problem that keep rapidly increasing. The main problems in order to resolve the problems, one of the current trust aware mechanism that includes rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the performances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the tw Keywords: recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly increasing. The main problems in the CF recommender system are sparsity and cold start. In order to resolve the problems, one of the current researches has been directed to the CF with echanism that includes trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the two algorithms.recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly CF recommender system are sparsity and cold start. In been directed to the CF with a trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the o algorithms.

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A trust-semantic fusion-based recommendation approach for e-business applications

Cited by 152 publications

References 22 publications

Connecting Researchers with Companies for University-Industry Collaboration

Connecting Researchers with Companies for University-Industry Collaboration

An Improved Trust-aware Recommender System for Personalized User Recommendation in Tmall

Trust aware recommender system with distrust in different views of trusted users

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