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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Integrating with Social Network to Enhance Recommender System Based-on Dempster-Shafer Theory

verfasst von : Van-Doan Nguyen, Van-Nam Huynh

Erschienen in: Computational Social Networks

Verlag: Springer International Publishing

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Abstract

In this paper, we developed a new collaborative filtering recommender system integrating with a social network that contains all users. In this system, user preferences and community preferences extracted from the social network are modeled as mass functions, and Dempster’s rule of combination is selected for fusing the preferences. Especially, with the community preferences, both the sparsity and cold-start problems are completely eliminated. So as to evaluate and demonstrate the advantage of the new system, we have conducted a range of experiments using Flixster data set.

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Vorheriges Kapitel Trust Evaluation Based Friend Recommendation in Proximity Based Mobile Social Network
Nächstes Kapitel Exploiting Social Relations to Recommend Scientific Publications
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Metadaten
Titel
Integrating with Social Network to Enhance Recommender System Based-on Dempster-Shafer Theory
verfasst von
Van-Doan Nguyen
Van-Nam Huynh
Copyright-Jahr
2016
Buch
Computational Social Networks

Print ISBN: 978-3-319-42344-9

Electronic ISBN: 978-3-319-42345-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42345-6_15