LCARS: A spatial item recommender system

Yin, Hongzhi, Cui, Bin, Sun, Yizhou, Hu, Zhiting and Chen, Ling (2014) LCARS: A spatial item recommender system. ACM Transactions on Information Systems, 32 3: 11-11. doi:10.1145/2629461

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Author Yin, Hongzhi
Cui, Bin
Sun, Yizhou
Hu, Zhiting
Chen, Ling
Title LCARS: A spatial item recommender system
Journal name ACM Transactions on Information Systems   Check publisher's open access policy
ISSN 1046-8188
1558-2868
Publication date 2014-07-01
Year available 2014
Sub-type Article (original research)
DOI 10.1145/2629461
Open Access Status Not yet assessed
Volume 32
Issue 3
Start page 11
End page 11
Total pages 37
Place of publication New York, NY United States
Publisher Association for Computing Machinery
Language eng
Formatted abstract
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.
Keyword Recommender system
TA algorithm
Cold Start
Location based service
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 23 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 51 times in Scopus Article | Citations
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Created: Sun, 01 Mar 2015, 21:39:46 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering