Results 141 to 150 of about 1,026 (182)

Clustering Users’ POIs Visit Trajectories for Next-POI Recommendation

2018
A 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
openaire   +2 more sources

Graph-Based Metric Embedding for Next POI Recommendation

2016
With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), point of interest (POI) recommendation has become an important means to help people discover attractive and interesting places.
Min Xie, Hongzhi Yin, Fanjiang Xu, Hao Wang 0005, Xiaofang Zhou 0001   +4 more
openaire   +4 more sources

Pre-Training Across Different Cities for Next POI Recommendation

ACM Transactions on the Web, 2023
The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across cities to alleviate those data sparsity and imbalance problems for next POI recommendation.
Ke Sun 0010, Tieyun Qian, Chenliang Li 0005, Xuan Ma, Qing Li 0001, Ming Zhong 0002, Yuanyuan Zhu 0001, Mengchi Liu   +7 more
openaire   +2 more sources

Social Personalized Ranking Embedding for Next POI Recommendation

2017
As the increasing popularity of the applications of location-based services, points-of-interest (POI) recommendation has become a great value part to help users explore their surrounding living environment and improve the quality of life. Recently, some researchers proposed next POI recommendation, which not only exploiting the users personal interests
Yan Long, Pengpeng Zhao 0001, Victor S. Sheng, Guanfeng Liu 0001, Jiajie Xu 0001, Jian Wu 0002, Zhiming Cui 0002   +6 more
openaire   +2 more sources

Harnessing a generalised user behaviour model for next-POI recommendation

Proceedings of the 12th ACM Conference on Recommender Systems, 2018
Recommender Systems (RSs) are commonly used in web applications to support users in finding items of their interest. In this paper we propose a novel RS approach that supports human decision making by leveraging data acquired in the physical world. We consider a scenario in which users' choices to visit points of interests (POIs) are tracked and used ...
David Massimo, Francesco Ricci 0001
openaire   +1 more source

Next-POI Recommendations for the Smart Destination Era

2019
A novel Recommender System exploiting behavioural users’ data in order to identify and recommend relevant and novel points of interest (POIs) is here presented. The proposed approach applies clustering to users’ sensed POI visit trajectories in order to identify like-behaving users and then it learns a distinct behaviour model for each cluster.
David Massimo, Francesco Ricci
openaire   +1 more source

DynaPosGNN: Dynamic-Positional GNN for Next POI Recommendation

2021 International Conference on Data Mining Workshops (ICDMW), 2021
Junbeom Kim, Sihyun Jeong, Goeon Park, Kihoon Cha, Ilhyun Suh, Byungkook Oh   +5 more
openaire   +2 more sources

Next POI Recommendation with Neighbor and Location Popularity

2023
Xianxian Li, Tianran Liu, Li-e Wang 0001, Zhigang Sun, Huachang Zeng   +4 more
openaire   +2 more sources

Adaptive Graph Representation Learning for Next POI Recommendation

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
Zhaobo Wang, Yanmin Zhu 0006, Chunyang Wang 0001, Wenze Ma, Bo Li 0001, Jiadi Yu   +5 more
openaire   +1 more source

Spatio-Temporal Hypergraph Learning for Next POI Recommendation

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
Xiaodong Yan, Tengwei Song, Yifeng Jiao, Jianshan He, Jiaotuan Wang, Ruopeng Li, Wei Chu   +6 more
openaire   +2 more sources