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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/75565
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/75565


    Title: A multi-theoretical kernel-based approach to social network-based recommendation
    Authors: Li, X.;Wang, M.;Liang, Ting-Peng
    梁定澎
    Contributors: 資管系
    Date: 2014-09
    Issue Date: 2015-06-04 13:53:38 (UTC+8)
    Abstract: Recommender systems are a critical component of e-commerce websites. The rapid development of online social networking services provides an opportunity to explore social networks together with information used in traditional recommender systems, such as customer demographics, product characteristics, and transactions. It also provides more applications for recommender systems. To tackle this social network-based recommendation problem, previous studies generally built trust models in light of the social influence theory. This study inspects a spectrum of social network theories to systematically model the multiple facets of a social network and infer user preferences. In order to effectively make use of these heterogonous theories, we take a kernel-based machine learning paradigm, design and select kernels describing individual similarities according to social network theories, and employ a non-linear multiple kernel learning algorithm to combine the kernels into a unified model. This design also enables us to consider multiple theories' interactions in assessing individual behaviors. We evaluate our proposed approach on a real-world movie review data set. The experiments show that our approach provides more accurate recommendations than trust-based methods and the collaborative filtering approach. Further analysis shows that kernels derived from contagion theory and homophily theory contribute a larger portion of the model. © 2014 Elsevier B.V. All rights reserved.
    Relation: Decision Support Systems, 65(Issue C), 95-104
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.dss.2014.05.006
    DOI: 10.1016/j.dss.2014.05.006
    Appears in Collections:[資訊管理學系] 期刊論文

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