A location-sentiment-aware recommender system for both home-town and out-of-town users

Wang, Hao , Fu, Yanmei , Wang, Qinyong , Yin, Hongzhi , Du, Changying and Xiong, Hui (2017). A location-sentiment-aware recommender system for both home-town and out-of-town users. In: KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Halifax, NS, Canada, (1135-1143). 13-17 August 2017. doi:10.1145/3097983.3098122


Author Wang, Hao
Fu, Yanmei
Wang, Qinyong
Yin, Hongzhi
Du, Changying
Xiong, Hui
Title of paper A location-sentiment-aware recommender system for both home-town and out-of-town users
Conference name ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
Conference location Halifax, NS, Canada
Conference dates 13-17 August 2017
Convener ACM
Proceedings title KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Journal name Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Series Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Place of Publication New York, NY, United States
Publisher ACM
Publication Year 2017
Sub-type Fully published paper
DOI 10.1145/3097983.3098122
Open Access Status Not yet assessed
ISBN 9781450348874
Volume Part F129685
Start page 1135
End page 1143
Total pages 9
Language eng
Abstract/Summary Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.
Subjects 1712 Software
1710 Information Systems
Keyword Check-in behavior
Crowd sentiment
Recommendation
User interest drift
Q-Index Code E1
Q-Index Status Provisional Code
Grant ID 61402447
61502466
61602453
61672501
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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Created: Thu, 14 Sep 2017, 23:00:51 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering