STELLAR: Spatial-temporal latent ranking for successive point-of-interest recommendation
Refereed conference paper presented and published in conference proceedings

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AbstractSuccessive point-of-interest (POI) recommendation in location-based social networks (LBSNs) becomes a significant task since it helps users to navigate a number of candidate POIs and provides the best POI recommendations based on users' most recent check-in knowledge. However, all existing methods for successive POI recommendation only focus on modeling the correlation between POIs based on users' check-in sequences, but ignore an important fact that successive POI recommendation is a time-subtle recommendation task. In fact, even with the same previous check-in information, users would prefer different successive POIs at different time. To capture the impact of time on successive POI recommendation, in this paper, we propose a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. In particular, the proposed STELLAR model is built upon a ranking-based pairwise tensor factorization framework with a fine-grained modeling of user-POI, POI-time, and POI-POI interactions for successive POI recommendation. Moreover, we propose a new interval-aware weight utility function to differentiate successive check-ins' correlations, which breaks the time interval constraint in prior work. Evaluations on two real-world datasets demonstrate that the STELLAR model outperforms state-of-the-art successive POI recommendation model about 20% in Precision@5 and Recall@5.
All Author(s) ListZhao S., Zhao T., Yang H., Lyu M.R., King I.
Name of Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Start Date of Conference12/02/2016
End Date of Conference17/02/2016
Place of ConferencePhoenix
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
Detailed descriptionorganized by AAAI Association ,
Pages315 - 321
LanguagesEnglish-United Kingdom

Last updated on 2021-05-12 at 23:48