Results 11 to 20 of about 2,209 (219)

A Diffusion Model for POI Recommendation

ACM Transactions on Information Systems, 2023
Next Point-of-Interest (POI) recommendation is a critical task in location-based services that aim to provide personalized suggestions for the user’s next destination. Previous works on POI recommendation have laid focus on modeling the user’s spatial preference.
Yifang Qin, Hongjun Wu 0006, Wei Ju 0001, Xiao Luo 0001, Ming Zhang 0004   +4 more
openaire   +3 more sources

Content-based POI Recommendation in Real-time Traffic Constraint [PDF]

Jisuanji gongcheng, 2017
Traditional Point of Interest(POI) recommendation method does not consider the user’s location and real-time traffic information.Though it can satisfy the user’s preference,it leads to a big increase in travel time.A recommended method of considering the
LEI Kai,LIU Shubo,LI Dan,LI Yongkai
doaj   +2 more sources

Personalized Geographical Influence Modeling for POI Recommendation [PDF]

IEEE Intelligent Systems, 2020
Point-of-interest (POI) recommendation has great significance in helping users find favorite places from a large number of candidate venues. One challenging in POI recommendation is to effectively exploit geographical information since users usually care about the physical distance to the recommended POIs.
Yanan Zhang, Guanfeng Liu 0001, An Liu 0002, Yifan Zhang, Zhixu Li, Xiangliang Zhang 0001, Qing Li 0001   +6 more
openaire   +3 more sources

Adapting to User Interest Drift for POI Recommendation

IEEE Transactions on Knowledge and Data Engineering, 2016
Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of research has focused on modeling user geographical preferences for POI recommendation, they ignore the phenomenon of user interest drift across geographical ...
Hongzhi Yin, Xiaofang Zhou 0001, Bin Cui 0001, Hao Wang 0005, Kai Zheng 0001, Nguyen Quoc Viet Hung   +5 more
openaire   +5 more sources

Geographically Insensitive Spatial-Temporal POI Recommendation Based on Heterogeneous Graph Embedding [PDF]

Jisuanji kexue yu tansuo
The increasingly large scale of location-based social networks (LBSN) promotes the rapid development of point-of-interest (POI) recommendation business.
LI Manwen, ZHANG Yueqin, ZHANG Chenwei, ZHANG Zehua
doaj   +2 more sources

POI Recommendation: A Temporal Matching between POI Popularity and User Regularity

2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the
Zijun Yao 0001, Yanjie Fu, Bin Liu 0045, Yanchi Liu, Hui Xiong 0001   +4 more
openaire   +2 more sources

Event-Based Probabilistic Embedding for POI Recommendation

Applied Sciences, 2023
Location-based social networks (LBSNs) have collected massive geo-tagged information, enabling the derivation of user preference for point of interests (POIs) in support of personalized recommendation. The existing embedding techniques deal with multiple
Tiancheng Zhang, Hengyu Liu, Xue Geng, Ge Yu   +3 more
doaj   +2 more sources

Self-Explainable Next POI Recommendation

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
Point-of-Interest (POI) recommendation involves predicting users' next preferred POI and is becoming increasingly significant in location-based social networks.
Kai Yang, Yi Yang 0042, Qiang Gao 0003, Ting Zhong, Yong Wang 0046, Fan Zhou 0002   +5 more
openaire   +2 more sources

Discovering Subsequence Patterns for Next POI Recommendation [PDF]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users.
Kangzhi Zhao, Yong Zhang 0002, Hongzhi Yin, Jin Wang 0007, Kai Zheng 0001, Xiaofang Zhou 0001, Chunxiao Xing   +6 more
openaire   +4 more sources

Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks

ISPRS International Journal of Geo-Information, 2019
Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations.
Mingxin Gan, Ling Gao
doaj   +2 more sources