Activity Recommendation with Partners

doi:10.1145/3121407

CORC > 上海财经大学 > 上海财经大学

	Activity Recommendation with Partners
	Tu, Wenting 1; Cheung, David W.2; Mamoulis, Nikos 2; Yang, Min 3; Lu, Ziyu 2
刊名	ACM TRANSACTIONS ON THE WEB
	2018-02
卷号	12 期号:1
关键词	Recommendation system Location-based social network
ISSN号	1559-1131
DOI	10.1145/3121407
英文摘要	Recommending social activities, such as watching movies or having dinner, is a common function found in social networks or e-commerce sites. Besides certain websites which manage activity-related locations (e.g., foursquare.com), many items on product sale platforms (e.g., groupon.com) can naturally be mapped to social activities. For example, movie tickets can be thought of as activity items, which can be mapped as a social activity of "watch a movie." Traditional recommender systems estimate the degree of interest for a target user on candidate items (or activities), and accordingly, recommend the top-k activity items to the user. However, these systems ignore an important social characteristic of recommended activities: people usually tend to participate in those activities with friends. This article considers this fact for improving the effectiveness of recommendation in two directions. First, we study the problem of activity-partner recommendation; i.e., for each recommended activity item, find a suitable partner for the user. This (i) saves the user's time for finding activity partners, (ii) increases the likelihood that the activity item will be selected by the user, and (iii) improves the effectiveness of recommender systems to users overall and enkindles their social enthusiasm. Our partner recommender is built upon the users' historical attendance preferences, their social context, and geographic information. Moreover, we explore how to leverage the partner recommendation to help improve the effectiveness of recommending activities to users. Assuming that users tend to select the activities for which they can find suitable partners, we propose a partner-aware activity recommendation model, which integrates this hypothesis into conventional recommendation approaches. Finally, the recommended items not only match users' interests, but also have high chances to be selected by the users, because the users can find suitable partners to attend the corresponding activities together. We conduct experiments on real data to evaluate the effectiveness of activity-partner recommendation and partner-aware activity recommendation. The results verify that (i) suggesting partners greatly improves the likelihood that a recommended activity item is to be selected by the target user and (ii) considering the existence of suitable partners in the ranking of recommended items improves the accuracy of recommendation significantly.
WOS研究方向	Computer Science
语种	英语
出版者	ASSOC COMPUTING MACHINERY
WOS记录号	WOS:000425712800004
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/700]
专题	上海财经大学
通讯作者	Tu, Wenting
作者单位	1.Shanghai Univ Finance & Econ, Sch Informat Management & Engn, 777 Guoding Rd, Shanghai, Peoples R China; 2.Univ Hong Kong, Comp Sci Dept, Pokfulam, Hong Kong, Peoples R China; 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Xueyuan Ave 1068, Shenzhen, Peoples R China
推荐引用方式 GB/T 7714	Tu, Wenting,Cheung, David W.,Mamoulis, Nikos,et al. Activity Recommendation with Partners[J]. ACM TRANSACTIONS ON THE WEB,2018,12(1).
APA	Tu, Wenting,Cheung, David W.,Mamoulis, Nikos,Yang, Min,&Lu, Ziyu.(2018).Activity Recommendation with Partners.ACM TRANSACTIONS ON THE WEB,12(1).
MLA	Tu, Wenting,et al."Activity Recommendation with Partners".ACM TRANSACTIONS ON THE WEB 12.1(2018).