標題: GLR: A graph-based latent representation model for successive POI recommendation
作者: Lu, Yi-Shu
Huang, Jiun-Long
資訊工程學系
Department of Computer Science
關鍵字: Successive POI recommendation;User preference;Successive transition influence;Geographical influence;Temporal influence;Regional influence
公開日期: 1-一月-2020
摘要: Point-of-Interest (POI) recommendation is an important service in location-based social networks (LBSNs) since it can help a user to discover new POIs for future visiting. In order to provide better recommendation experience, a novel POI recommendation paradigm, named successive POI recommendation, has been proposed. The difference between traditional POI recommendation and successive POI recommendation is that successive POI recommendation focuses on recommending POIs that the target user may like to visit within a time period (e.g., a few hours). To address this problem, we propose a new graph-based latent representation model called GLR, to obtain the latent vectors of temporal successive transition influence and temporal user preference based on the historical check-in records. We also propose a novel method named GLR_GT to employ these latent vectors and geographical influence of POIs to perform successive POI recommendation. Moreover, we also propose an extended method named GLR_GT_LSTM to employ a long short-term memory (LSTM) neural network to model users' complex transition behavior. Several experiments are conducted on some real-world LBSN datasets. Experimental results show that our proposed method GLR_GT_LSTM outperforms the other prior successive POI recommendation methods in terms of precision and recall. (C) 2019 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.future.2019.07.074
http://hdl.handle.net/11536/153360
ISSN: 0167-739X
DOI: 10.1016/j.future.2019.07.074
期刊: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume: 102
起始頁: 230
結束頁: 244
顯示於類別:期刊論文