Abstract

The pervasive presence of social media greatly enriches online users' social activities, resulting in abundant social relations. Social relations provide an independent source for recommendation, bringing about new opportunities for recommender systems. Exploiting social relations to improve recommendation performance attracts a great amount of attention in recent years. Most existing social recommender systems treat social relations homogeneously and make use of direct connections (or strong dependency connections). However, connections in online social networks are intrinsically heterogeneous and are a composite of various relations. While connected users in online social networks form groups, and users in a group share similar interests, weak dependency connections are established among these users when they are not directly connected. In this paper, we investigate how to exploit the heterogeneity of social relations and weak dependency connections for recommendation. In particular, we employ social dimensions to simultaneously capture heterogeneity of social relations and weak dependency connections, and provide principled ways to model social dimensions, and propose a recommendation framework SoDimRec which incorporates heterogeneity of social relations and weak dependency connections based on social dimensions. Experimental results on real-world data sets demonstrate the effectiveness of the proposed framework. We conduct further experiments to understand the important role of social dimensions in the proposed framework.

Original languageEnglish (US)
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Pages251-257
Number of pages7
ISBN (Electronic)9781577357605
StatePublished - 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Other

Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
CountryUnited States
CityPhoenix
Period2/12/162/17/16

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Recommender systems
Composite materials
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Tang, J., Wang, S., Hu, X., Yin, D., Bi, Y., Chang, Y., & Liu, H. (2016). Recommendation with social dimensions. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 251-257). AAAI press.

Recommendation with social dimensions. / Tang, Jiliang; Wang, Suhang; Hu, Xia; Yin, Dawei; Bi, Yingzhou; Chang, Yi; Liu, Huan.

30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, 2016. p. 251-257.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tang, J, Wang, S, Hu, X, Yin, D, Bi, Y, Chang, Y & Liu, H 2016, Recommendation with social dimensions. in 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, pp. 251-257, 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, United States, 2/12/16.
Tang J, Wang S, Hu X, Yin D, Bi Y, Chang Y et al. Recommendation with social dimensions. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press. 2016. p. 251-257
Tang, Jiliang ; Wang, Suhang ; Hu, Xia ; Yin, Dawei ; Bi, Yingzhou ; Chang, Yi ; Liu, Huan. / Recommendation with social dimensions. 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, 2016. pp. 251-257
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