Abstract
Successive point-of-interest (POI) recommendation is an important research task which can recommend new POIs the user has not visited before. However, the existing researches for new successive POI recommendation ignore the integration of time information and social relations information which can improve the prediction of the system. In order to solve this problem, we propose a new recommendation model called PRME-GTS that incorporates social relations and temporal information in this paper. It can models the relations between users, temporal information, points of interest, and social information, which is based on the framework of pair-wise ranking metric embedding. Experimental results on the two datasets demonstrate that employing temporal information and social relations information can effectively improve the performance of the successive point-of-interest (POI) recommendation.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61972135, No. 61602159), the Natural Science Foundation of Heilongjiang Province (No. F201430), the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094, No. 2017RAQXJ131), and the fundamental research funds of universities in Heilongjiang Province, special fund of Heilongjiang University (No. HDJCCX-201608, No. KJCX201815, No. KJCX201816).
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Mao, R., Han, Z., Liu, Z., Liu, Y., Lv, X., Xuan, P. (2019). PRME-GTS: A New Successive POI Recommendation Model with Temporal and Social Influences. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_19
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DOI: https://doi.org/10.1007/978-3-030-35231-8_19
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