Journal article
Next and Next New POI Recommendation via Latent Behavior Pattern Inference
ACM Transactions on Information Systems (TOIS), Vol.37(4), 46
2019
Abstract
Next and next new point-of-interest (POI) recommendation are essential instruments in promoting customer experiences and business operations related to locations. However, due to the sparsity of the check-in records, they still remain insufficiently studied. In this article, we propose to utilize personalized latent behavior patterns learned from contextual features, e.g., time of day, day of week, and location category, to improve the effectiveness of the recommendations. Two variations of models are developed, including GPDM, which learns a fixed pattern distribution for all users; and PPDM, which learns personalized pattern distribution for each user. In both models, a soft-max function is applied to integrate the personalized Markov chain with the latent patterns, and a sequential Bayesian Personalized Ranking (S-BPR) is applied as the optimization criterion. Then, Expectation Maximization (EM) is in charge of finding optimized model parameters. Extensive experiments on three large-scale commonly adopted real-world LBSN data sets prove that the inclusion of location category and latent patterns helps to boost the performance of POI recommendations. Specifically, our models in general significantly outperform other state-of-the-art methods for both next and next new POI recommendation tasks. Moreover, our models are capable of making accurate recommendations regardless of the short/long duration or distance.
Details
- Title
- Next and Next New POI Recommendation via Latent Behavior Pattern Inference
- Authors
- Xin Li (Author) - Beijing Institute of Technology, ChinaDongcheng Han (Author) - Beijing Institute of Technology, ChinaJing He (Author) - Beijing Institute of Technology, ChinaLejian Liao (Author) - Beijing Institute of Technology, ChinaMingzhong Wang (Author) - University of the Sunshine Coast
- Publication details
- ACM Transactions on Information Systems (TOIS), Vol.37(4), 46; 28
- Publisher
- Association for Computer Machinery
- Date published
- 2019
- DOI
- 10.1145/3354187
- ISSN
- 1046-8188
- Organisation Unit
- University of the Sunshine Coast, Queensland; USC Business School - Legacy; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99451320502621
- Output Type
- Journal article
Metrics
99 Record Views
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Computer Science, Information Systems