LCARS: a location-content-aware recommender system

Yin, Hongzhi, Sun, Yizhou, Cui, Bin, Hu, Zhiting and Chen, Ling (2013). LCARS: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 19th ACM SIGKDD Knowledge Discovery and Data Mining, Chicago, IL, United States, (221-229). 11-14 August 2013. doi:10.1145/2487575.2487608

Author Yin, Hongzhi
Sun, Yizhou
Cui, Bin
Hu, Zhiting
Chen, Ling
Title of paper LCARS: a location-content-aware recommender system
Conference name 19th ACM SIGKDD Knowledge Discovery and Data Mining
Conference location Chicago, IL, United States
Conference dates 11-14 August 2013
Convener KDD
Proceedings title Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Place of Publication New York, NY, United States
Publisher ACM
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1145/2487575.2487608
Open Access Status Not yet assessed
ISBN 9781450321747
Start page 221
End page 229
Abstract/Summary 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 for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) 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 co-occurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. 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.
Keyword Recommender system
Location-based service
Probabilistic generative model
TA algorithm
Cold start
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collection: School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: Scopus Citation Count Cited 150 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sun, 01 Mar 2015, 21:52:43 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering