Modeling Location-Based User Rating Profiles for Personalized   Recommendation

doi:10.1145/2663356

CORC > 北京大学 > 信息科学技术学院

	Modeling Location-Based User Rating Profiles for Personalized Recommendation
	Yin, Hongzhi ; Cui, Bin ; Chen, Ling ; Hu, Zhiting ; Zhang, Chengqi
刊名	ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
	2015
关键词	Algorithms Design Experimentation Performance User profile recommender system cold start probabilistic generative model location-based services SYSTEMS
DOI	10.1145/2663356
英文摘要	This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-k recommendation and the cold start problem.; National Natural Science Foundation of China [61272155]; 973 program [2014CB340405]; Australian Research Council (ARC) [DP120102829]; ARC Discovery Project [DP140100545]; Chinese National "111" project, "Attracting International Talents in Data Engineering and Knowledge Engineering Research"; SCI(E); ARTICLE; h.yin1@uq.edu.au; bin.cui@pku.edu.cn; ling.chen@uts.edu.au; zhitinghu@gmail.com; chengqi.zhang@uts.edu.au; 3,SI; 9
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/420595]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yin, Hongzhi,Cui, Bin,Chen, Ling,et al. Modeling Location-Based User Rating Profiles for Personalized Recommendation[J]. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA,2015.
APA	Yin, Hongzhi,Cui, Bin,Chen, Ling,Hu, Zhiting,&Zhang, Chengqi.(2015).Modeling Location-Based User Rating Profiles for Personalized Recommendation.ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA.
MLA	Yin, Hongzhi,et al."Modeling Location-Based User Rating Profiles for Personalized Recommendation".ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2015).