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Title: Item Silk Road: Recommending Items from Information Domains to Social Users
Authors: Xiang Wang 
Xiangnan He 
Liqiang Nie 
Tat-Seng Chua 
Keywords: Cross-domain Recommendation
Deep Collaborative Filtering
Deep Learning
Neural Network
Issue Date: 7-Aug-2017
Publisher: Association for Computing Machinery, Inc
Citation: Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua (2017-08-07). Item Silk Road: Recommending Items from Information Domains to Social Users. ACM SIGIR 2017 : 185-194. ScholarBank@NUS Repository.
Abstract: Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or Ecommerce sites that emphasize user-item interactions, like and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before. Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method. © 2017 Copyright held by the owner/author(s).
Source Title: ACM SIGIR 2017
ISBN: 9781450350228
DOI: 10.1145/3077136.3080771
Appears in Collections:Staff Publications

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