Abstract
Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of locationbased social networks in recent years. Compared with traditional tasks, it focuses more on personalized, context-aware recommendation results to provide better user experience. To address this new challenge, we propose a Collaborative Filtering method based on Nonnegative Tensor Factorization, a generalization of the Matrix Factorization approach that exploits a high-order tensor instead of traditional User-Location matrix to model multi-dimensional contextual information. The factorization of this tensor leads to a compact model of the data which is specially suitable for context-aware POI recommendations. In addition, we fuse users' social relations as regularization terms of the factorization to improve the recommendation accuracy. Experimental results on real-world datasets demonstrate the effectiveness of our approach.
Original language | English |
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Title of host publication | SIGIR 2015 |
Subtitle of host publication | Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 1007-1010 |
Number of pages | 4 |
ISBN (Electronic) | 9781450336215 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015 - Santiago, Chile Duration: 9 Aug 2015 → 13 Aug 2015 |
Other
Other | 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015 |
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Country/Territory | Chile |
City | Santiago |
Period | 9/08/15 → 13/08/15 |
Keywords
- Tensor factorization
- social regularization
- location based social networks
- recommendation