LCARS: A Spatial Item Recommender System

doi:10.1145/2629461

CORC > 北京大学 > 信息科学技术学院

	LCARS: A Spatial Item Recommender System
	Yin, Hongzhi ; Cui, Bin ; Sun, Yizhou ; Hu, Zhiting ; Chen, Ling
刊名	acm transactions on information systems
	2014
关键词	Algorithms Design Experimentation Performance Recommender system location-based service probabilistic generative model TA algorithm cold start DATABASES ALGORITHMS QUERIES
DOI	10.1145/2629461
英文摘要	Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge to the traditional collaborative filtering-based recommender systems.. The problem becomes even more challenging when people travel to a new city where they have no activity information. In this article, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants and shopping malls) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item cooccurrence patterns and exploiting item contents. The online recommendation part takes a querying user along with a querying city as input, and automatically combines the learned interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up the online process, a scalable query processing technique is developed by extending both the Threshold Algorithm (TA) and TA-approximation algorithm. We evaluate the performance of our recommender system on two real datasets, that is, DoubanEvent and Foursquare, and one large-scale synthetic dataset. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency. Besides, the experimental analysis results also demonstrate the excellent interpretability of LCARS.; Computer Science, Information Systems; SCI(E); 0; ARTICLE; bin.cui@pku.edu.cn; bin.cui@pku.edu.cn; 3; 32
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/389193]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yin, Hongzhi,Cui, Bin,Sun, Yizhou,et al. LCARS: A Spatial Item Recommender System[J]. acm transactions on information systems,2014.
APA	Yin, Hongzhi,Cui, Bin,Sun, Yizhou,Hu, Zhiting,&Chen, Ling.(2014).LCARS: A Spatial Item Recommender System.acm transactions on information systems.
MLA	Yin, Hongzhi,et al."LCARS: A Spatial Item Recommender System".acm transactions on information systems (2014).