Results 181 to 190 of about 2,209 (219)
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Capturing Geographical Influence in POI Recommendations
2013Point-of-Interest POI recommendation is a significant service for location-based social networks LBSNs. It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users' interests depends on three factors: user preference, social influence, and geographical influence.
Shenglin Zhao +2 more
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Hierarchical POI Attention Model for Successive POI Recommendation
2021The rapid growth of location-based social networks developed a large number of point-of-interests (POIs). POI recommendation task aims to predict users’ successive POIs, which has attracted more and more research interests recently. POI recommendation is achieved based on POI context, which contains a variety of information, including check-in sequence
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Deep Transfer Learning for Successive POI Recommendation
2021Personalized POI recommendation attracts more and more attention from both industrial and research fields. Due to data collection mechanism, it is common to see data collection with the unbalanced spatial distribution. For example, some cities may release check-ins for multiple years while others only release a few days of data.
Haining Tan, Di Yao 0001, Jingping Bi
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Current location-based next POI recommendation
Proceedings of the International Conference on Web Intelligence, 2017Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours.
Shokirkhon Oppokhonov +2 more
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A Context-Aware POI Recommendation
TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), 2021Tipajin Thaipisutikul, Ying-Nong Chen
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Flexible POI Recommendation Based on User Situation
2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2019Location-based social networks (LBSNs) become an essential part of our lives as these services can assist users in finding interesting point-of-interest (POI). Many studies have been conducted to perform POI recommendations with various factors, such as user's check-in records, geographic information, and social relationship.
Sein Jang +2 more
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Real-time event embedding for POI recommendation
Neurocomputing, 2019Abstract Location-based social networks (LBSNs) allow users to check-in and share daily lives with others. We have witnessed very rapid development of LBSNs in recent years. Point-of-Interest (POI) recommendation is one of the core services in LBSNs. In this study, we propose a real-time POI embedding model. Instead of capturing intrinsic information,
Pei-Yi Hao +2 more
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Online meta-learning for POI recommendation
GeoInformatica, 2022Yao Lv +7 more
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Context-Aware Personalized POI Sequence Recommendation
2019The Point Of Interest (POI) sequence recommendation applies to scenarios like itinerary and travel route planning which belongs to the class of NP-hard problem. What’s more, the external environment like the weather, time can affect the user’s check-in behavior such as people prefer to check-in in ice cream shop when the temperature is higher.
Jing Chen 0003, Wenjun Jiang
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Understanding the Impact of Weather for POI Recommendations [PDF]
ACM RecSys Workshop on Recommenders in Tourism, 2016 Trattner, Christoph +4 more
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