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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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Clustering Users’ POIs Visit Trajectories for Next-POI Recommendation
2018A novel recommender system that supports tourists in choosing interesting and novel points of interests (POIs) is here presented. It can deal with situations where users’ data is scarce and there is no additional information about users apart from their past POIs visits.
David Massimo, Francesco Ricci 0001
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On accurate POI recommendation via transfer learning
Distributed and Parallel Databases, 2020Point of interest (POI) recommendation is of great value for both service providers and users. However, it is hard due to data scarcity. To this end, in this paper, we propose a transfer learning based deep neural model, which fuses valueable cross-domain knowledge to achieve more accurate POI recommendation.
Hao Zhang +4 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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POI recommendation with geographical and multi-tag influences
2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), 2016In this paper, we propose a method for point of interest (POI) recommendation by extracting the multi-tag influence and modeling the geographical influence. First of all, we extract a user-tag matrix from the initial user-POI rating matrix by analyzing the relations between POI and the related bag of tags.
Zhiyuan Zhang 0003 +3 more
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Property Analysis of Stay Points for POI Recommendation
2021Stay points extracted from trajectories are often treated as points of interest (POI) in the data preprocessing of POI recommendations. Popularity (i.e., the number of visits) is one of the important features to distinguish the value of different POIs, especially for tourists traveling in an unfamiliar city.
Junjie Sun +2 more
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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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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 User-Side POIs Mobile Recommender System
2021Recommending pertinent Place Of Interests (POIs) is a desirable feature for mobile users, and which is generally served by for-profit proprietary platforms, such as Yelp, TripAdvisor, etc. However, the siloed design of these platforms raises today several issues about privacy, user data portability, and algorithm transparency.
Mohamed Boubenia +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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