Results 221 to 230 of about 243,536 (257)

Point of interest recommendation with social and geographical influence

2016 IEEE International Conference on Big Data (Big Data), 2016
Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat. The large number of check-in histories make it possible to mine the preference of each user and then to provide ...
Da-Chuan Zhang, Mei Li, Chang-Dong Wang 0001   +2 more
openaire   +1 more source

Context aware point of interest adaptive recommendation

Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation, 2012
Applications that allow the users to search for nearby points of interest have, recently, become very popular amongst mobile device users. However, the increasing amount of available information and the limitations of current mobile devices can hinder an efficient and helpful user experience. It is fundamental that what is shown to the user is relevant.
Paulo Pombinho, Maria Beatriz Carmo, Ana Paula Afonso 0001   +2 more
openaire   +1 more source

Points of interest recommendation from GPS trajectories

International Journal of Geographical Information Science, 2015
Recently, points of interest POIs recommendation has evolved into a hot research topic with real-world applications. In this paper, we propose a novel semantics-enhanced density-based clustering algorithm SEM-DTBJ-Cluster, to extract semantic POIs from GPS trajectories.
Yaqiong Liu, Hock Soon Seah
openaire   +1 more source

Point-of-Interest Recommendations

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016
The emergence of Location-based Social Network (LBSN) services provides a wonderful opportunity to build personalized Point-of-Interest (POI) recommender systems. Although a personalized POI recommender system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model user's POI decision ...
Huayu Li, Yong Ge 0001, Richang Hong, Hengshu Zhu   +3 more
openaire   +1 more source

A Geographical Behavior-Based Point-of-Interest Recommendation

2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2019
With the development of mobile devices, point-of-interest (POI) recommendation has received increasing attention. However, achieving accurate personalized POI recommendation is challenging due to the sparsity of the available data per user. In addition, previous efforts based on collaborative filtering mainly treat user behavior as a whole part in ...
Xiaoyun Yu, Xin Li 0033, Jidong Li, Keke Gai   +3 more
openaire   +1 more source

Point-of-Interest Recommendations by Unifying Multiple Correlations

2016
In recent years, we have witnessed the development of location-based services which benefit users and businesses. This paper aims to provide a unified framework for location-aware recommender systems with the consideration of social influence, categorical influence and geographical influence for users’ preference.
Ce Cheng, Jiajin Huang, Ning Zhong 0001
openaire   +1 more source

APPR: Additive Personalized Point-of-Interest Recommendation

2018 IEEE Global Communications Conference (GLOBECOM), 2018
Providing location recommendations has become an essential feature for location-based social networks (LBSNs), as it helps the users to explore new places and makes LBSNs more prevalent to them. Existing studies mostly focus on introducing the new features that affect users' check-in behaviours in LBSNs.
Elahe Naserianhanzaei, Xinheng Wang 0001, Keshav P. Dahal   +2 more
openaire   +1 more source

Recommendation of Points-of-Interest Using Graph Embeddings

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018
The rapid growth of Location-based Social Networks (LBSNs) has lead to the generation of massive datasets which are collected in an exponential rate. The collected information may be used to facilitate users' needs with recommendations related to their past preferences.
Giannis Christoforidis, Pavlos Kefalas, Apostolos Papadopoulos, Yannis Manolopoulos   +3 more
openaire   +1 more source

Learning geographical preferences for point-of-interest recommendation

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013
The problem of point of interest (POI) recommendation is to provide personalized recommendations of places of interests, such as restaurants, for mobile users. Due to its complexity and its connection to location based social networks (LBSNs), the decision process of a user choose a POI is complex and can be influenced by various factors, such as user ...
Bin Liu 0045, Yanjie Fu, Zijun Yao 0001, Hui Xiong 0001   +3 more
openaire   +1 more source

Neural embedding features for point-of-interest recommendation

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2019
The focus of point-of-interest recommendation techniques is to suggest a venue to a given user that would match the users' interests and is likely to be adopted by the user. Given the multitude of venues and the sparsity of user check-ins, the problem of recommending venues has shown to be a difficult task.
Alireza Pourali, Fattane Zarrinkalam, Ebrahim Bagheri   +2 more
openaire   +1 more source