通知公告

学术报告通知(编号:2017-21)

2017-05-18

       报告题目:Item Silk Road: Recommending Items from Information Domains to Social Users
 

       报告人:何向南 博士

      

       单位:新加坡国立大学
 

       报告时间:2017年5月22日(周一) 下午15:00
 

       报告地点:学术会议中心二楼小报告厅

 

       报告简介:
       Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com 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.

 

       报告人简介:
       Dr. Xiangnan He is currently a postdoctoral research fellow with the Lab for Media Search, National University of Singapore. His research interests span recommender system, information retrieval, multi-media and natural language processing. He has over 20 publications appeared in several top-tier conferences such as SIGIR, WWW, MM, CIKM, IJCAI and AAAI, and top-tier journals including TKDE and TOIS. His work on recommender system has received the Best Paper Award Honorable Mention of ACM SIGIR 2016. Moreover, he has served as the PC member for the prestigious conferences including SIGIR, WWW, MM, CIKM and EMNLP, the Co-chair of CIKM 2017 workshop on "Social Media Analytics for Smart Cities", and the invited reviewer for prestigious journals including TKDE, TOIS, WWWJ and TIIS.
 

 

计算机与信息学院

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