Results 261 to 270 of about 459,897 (295)

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

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

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

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

Local Point of Interest Recommendation Based on Probability

2016
The popularity of smart phones and the maturity of positioning technology spawn location-based social network (LBSN). The vast amounts of information assigns the location-based social network recommendation system a pivotal role. Current local point of interest recommendation algorithms often encounter two problems: synonymous site recognition and ...
Miao Zhang, Shiqun Yin, Meng-Meng Sun, Sheng Gao   +3 more
openaire   +1 more source

Exploiting geographical influence for collaborative point-of-interest recommendation

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011
In this paper, we aim to provide a point-of-interests (POI) recommendation service for the rapid growing location-based social networks (LBSNs), e.g., Foursquare, Whrrl, etc. Our idea is to explore user preference, social influence and geographical influence for POI recommendations.
Mao Ye 0002, Peifeng Yin, Wang-Chien Lee, Dik Lun Lee   +3 more
openaire   +1 more source

Unified Point-of-Interest Recommendation with Temporal Interval Assessment

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016
Point-of-interest (POI) recommendation, which helps mobile users explore new places, has become an important location-based service. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence.
Yanchi Liu, Chuanren Liu, Bin Liu 0045, Meng Qu, Hui Xiong 0001   +4 more
openaire   +1 more source

Decentralized Point-Of-Interest (POI) Recommender Systems

The Next Point-Of-Interest (POI) recommendation has gained prominence with the rapid expansion of Location-Based Social Networks (LBSNs), due to its effectiveness in leveraging historical check-in data to predict users' next visiting POIs. While current centralized POI recommenders based on Recurrent Neural Networks (RNNs) and Graph Neural Networks ...
openaire   +2 more sources

Point-of-Interest Group Recommendation with an Extreme Learning Machine

2019
With the increasing popularity of location-based social networks (LBSNs), an increasing number of people are sharing their locations with friends through check-in activities. Point-of-interest (POI) recommendation, in which new places are suggested to users, is one of the most important tasks in LBSNs.
Zhen Zhang, Guoren Wang, Xiangguo Zhao
openaire   +1 more source