1. NCCU Academic Hub
  2. 學術產出
  3. 商學院
  4. 期刊論文
  5. Item

學術產出-期刊論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 A multi-theoretical kernel-based approach to social network-based recommendation
作者 Li, X.;Wang, M.;Liang, Ting-Peng
梁定澎
貢獻者 資管系
日期 2014-09
上傳時間 4-六月-2015 13:53:38 (UTC+8)
摘要 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.
關聯 Decision Support Systems, 65(Issue C), 95-104
資料類型 article
DOI http://dx.doi.org/10.1016/j.dss.2014.05.006
dc.contributor 資管系
dc.creator (作者) Li, X.;Wang, M.;Liang, Ting-Peng
dc.creator (作者) 梁定澎zh_TW
dc.date (日期) 2014-09
dc.date.accessioned 4-六月-2015 13:53:38 (UTC+8)-
dc.date.available 4-六月-2015 13:53:38 (UTC+8)-
dc.date.issued (上傳時間) 4-六月-2015 13:53:38 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75565-
dc.description.abstract (摘要) 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.
dc.format.extent 878091 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Decision Support Systems, 65(Issue C), 95-104
dc.title (題名) A multi-theoretical kernel-based approach to social network-based recommendation
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.dss.2014.05.006
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.dss.2014.05.006