Results 11 to 20 of about 459,897 (295)

Embracing LLMs for Point-of-Interest Recommendations

IEEE Intelligent Systems
A point-of-interest (POI) recommendation becomes the core function of location-based services. Unlike a traditional item recommendation, a POI recommendation has distinct features, such as geographical influences, complex mobility patterns, and a balance between local and global user preferences.
Tianxing Wang 0004, Can Wang 0004
openaire   +3 more sources

AST-PG: Attention-Based Spatial–Temporal Point-of-Interest-Group Model for Real-Time Point-of-Interest Recommendation

Applied Sciences
Research on next-point-of-interest (POI) recommendation has become a new focus in the field of POI recommendation in recent years. The goal of POI recommendation tasks is to predict a user’s future movement trajectory based on their current state and ...
Huarui Yu, Zesheng Cheng
doaj   +2 more sources

Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation

Algorithms
Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses.
Juan Chen, Qiao Li
doaj   +2 more sources

Online Evaluation of Point-Of-Interest Recommendation Systems. [PDF]

, 2015
In this work we describe a system to evaluate multiple point- of-interest recommendation systems. In this system each recommendation service will be exposed online and crowd-sourced assessors will interact with merged results from multiple services, which are responding to suggestion requests live, in order to determine which system performs best. This
Dean-Hall, A., Clarke, C.L.A., Kamps, J., Kiseleva, J.   +3 more
core   +4 more sources

Spatio-Temporal Transformer Recommender: Next Location Recommendation with Attention Mechanism by Mining the Spatio-Temporal Relationship between Visited Locations

ISPRS International Journal of Geo-Information, 2023
Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of ...
Shuqiang Xu, Qunying Huang, Zhiqiang Zou
doaj   +1 more source

A BiLSTM-attention-based point-of-interest recommendation algorithm

Journal of Intelligent Systems, 2023
Aiming at the problem that users’ check-in interest preferences in social networks have complex time dependences, which leads to inaccurate point-of-interest (POI) recommendations, a location-based POI recommendation model using deep learning for social ...
Li Aichuan, Liu Fuzhi
doaj   +1 more source

Human Mobility Pattern Prior Knowledge Based POI Recommendation [PDF]

Jisuanji kexue, 2023
Point of interest(POI) recommendation is a fundamental task in location-based social networks,which provides users with personalized place recommendations.However,the current point of interest recommendation is mostly based on learning the user's check ...
YI Qiuhua, GAO Haoran, CHEN Xinqi, KONG Xiangjie
doaj   +1 more source

On recommendation problems beyond points of interest [PDF]

Information Systems, 2015
Recommendation systems aim to recommend items or packages of items that are likely to be of interest to users. Previous work on recommendation systems has mostly focused on recommending points of interest (POI), to identify and suggest top-k items or packages that meet selection criteria and satisfy compatibility constraints on items in a package ...
Ting Deng, Wenfei Fan, Floris Geerts
openaire   +2 more sources

Review Recommendation for Points of Interest's Owners [PDF]

Proceedings of the 28th ACM Conference on Hypertext and Social Media, 2017
Websites that provide reviews for services and products deal with big volumes of data (many users writing many reviews for many items). Then, recommendation algorithms come to the rescue in matching reviews to the consumers who are reading them. Such online review applications usually recommend the most useful reviews for consumers to read.
Thiago R. P. Prado, Mirella M. Moro
openaire   +1 more source

Point-of-interest recommendation algorithm integrating multiple impact factors

Journal of Hebei University of Science and Technology, 2020
In order to solve the problem of data sparseness in the task of point-of-interest recommendation and make full use of the diverse information in the location-based social network to further improve the quality of personalized recommendation, a point-of ...
Huicong WU, Jiaoe LI, Mingxing ZHAO, Kai GAO   +3 more
doaj   +1 more source