Social influence attentive neural network for friend-enhanced recommendation

• Main Authors: LU, Yuanfu, XIE, Ruobing, SHI, Chuan, FANG, Yuan, WANG, Wei, ZHANG, Xu, LIN, Leyu
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2020
• 主題: Heterogeneous Graph; Friend-Enhanced Recommendation; Social Inuence; Databases and Information Systems; OS and Networks
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/5156; https://ink.library.smu.edu.sg/context/sis_research/article/6159/viewcontent/ECML20_SIAN.pdf

With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called FriendEnhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change the recommendation paradigm, making users to pay more attention to enhanced social factors. In this paper, we first formulate the FER problem, and propose a novel Social Influence Attentive Neural network (SIAN) solution. In order to fuse rich heterogeneous information, the attentive feature aggregator in SIAN is designed to learn user and item representations at both node- and typelevels. More importantly, a social influence coupler is put forward to capture the influence of the friend referral circle in an attentive manner. Experimental results demonstrate that SIAN outperforms several stateof-the-art baselines on three real-world datasets. (Code and dataset are available at https://github.com/rootlu/SIAN.