Modeling location-based user rating profiles for personalized recommendation

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Zhang, Chengqi (2015) Modeling location-based user rating profiles for personalized recommendation. ACM Transactions on Knowledge Discovery from Data, 9 3: 19:1-19:41. doi:10.1145/2663356

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Author Yin, Hongzhi
Cui, Bin
Chen, Ling
Hu, Zhiting
Zhang, Chengqi
Title Modeling location-based user rating profiles for personalized recommendation
Journal name ACM Transactions on Knowledge Discovery from Data   Check publisher's open access policy
ISSN 1556-4681
Publication date 2015-04-01
Year available 2015
Sub-type Article (original research)
DOI 10.1145/2663356
Open Access Status Not yet assessed
Volume 9
Issue 3
Start page 19:1
End page 19:41
Total pages 41
Place of publication New York, NY United States
Publisher ACM Special Interest Group
Language eng
Subject 1700 Computer Science
Abstract 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 locationbased 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.
Formatted abstract
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.
Keyword User profile
Recommender system
Cold start
Probabilistic generative model
Location-based services
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 13 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 39 times in Scopus Article | Citations
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Created: Sun, 01 Mar 2015, 21:31:57 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering