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Location Regularization-Based POI Recommendation in Location-Based Social Networks

Lei Guo, Haoran Jiang, Xinhua Wang
2018 Information  
In fact, in most location-based social networks, the user's negative preferences are not explicitly observable.  ...  Hence, this work concentrates on exploiting the geographical characteristics from a location perspective for implicit feedback, where a neighborhood aware Bayesian personalized ranking method (NBPR) is  ...  First, we describe the scenario of POI recommendation in location-based social networks.  ... 
doi:10.3390/info9040085 fatcat:s2yel2kdsvf33blmwnxvmb7ehy

Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks

Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing
2019 Complexity  
network and a predefined location network, respectively.  ...  According to the above observations, to well utilize the network information, a neural network-based embedding method (node2vec) is first exploited to learn the user and POI representations from a social  ...  Location-Aware Social Recommendation. e pretrained location network embeddings can be seen as the representations of POIs from geographical relations.  ... 
doi:10.1155/2019/3574194 fatcat:yvdlqwr77jahlovqada6e2zs2e

User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art [article]

Shudong Liu
2017 arXiv   pre-print
Location-based social networks(LBSNs)allow users to check-in at a physical location and share daily tips on points-of-interest (POIs) with their friends anytime and anywhere.  ...  Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling  ...  Location-based social networks (LBSNs) allow users to check-in and share their locations, tips, and experiences about points-of-interest (POIs) with their friends anytime and anywhere.  ... 
arXiv:1712.06768v1 fatcat:nzmsjj6kjzby7ldi3czf6zlkye

Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization

Lina Yao, Quan Z. Sheng, Yongrui Qin, Xianzhi Wang, Ali Shemshadi, Qi He
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of locationbased social networks in recent years.  ...  In addition, we fuse users' social relations as regularization terms of the factorization to improve the recommendation accuracy.  ...  INTRODUCTION With the popularization of mobile devices, wireless networks and location-enabling techniques, location-based social networks (LBSNs), such as Foursquare, Gowalla, and Brightkite, have been  ... 
doi:10.1145/2766462.2767794 dblp:conf/sigir/YaoSQWSH15 fatcat:rpc3rfcafrfvnmuephuebjes3u

A Graph-Based Taxonomy of Recommendation Algorithms and Systems in LBSNs

Pavlos Kefalas, Panagiotis Symeonidis, Yannis Manolopoulos
2016 IEEE Transactions on Knowledge and Data Engineering  
Recently, location-based social networks (LBSNs) gave the opportunity to users to share geo-tagged information along with photos, videos, and SMSs.  ...  Index Terms-Recommender systems, location-based recommendations Ç The authors are with the  ...  This subset of On-line Social Networks (OSNs) is known as Location-based Social Networks (LBSNs).  ... 
doi:10.1109/tkde.2015.2496344 fatcat:6gikvhjovvaj5dqvsu7tqvayu4

LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation [article]

Hossein A. Rahmani, Mohammad Aliannejadi, Sajad Ahmadian, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani
2019 arXiv   pre-print
With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings.  ...  To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view.  ...  Introduction With the spread of smartphones and other mobile devices, Location-Based Social Networks (LBSNs) have become very popular.  ... 
arXiv:1909.06667v1 fatcat:7mbarl3hxzb3nbzbinfiublldi

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation [article]

Kosar Seyedhoseinzadeh, Hossein A. Rahmani, Mohsen Afsharchi, Mohammad Aliannejadi
2022 arXiv   pre-print
More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision  ...  We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users' geographical activity centers.  ...  LGLMF selects POIs that are near to the user's most visited POI from a user's perspective and applies the impact of neighbor locations from a location perspective.  ... 
arXiv:2201.03450v1 fatcat:obftk7npzfdn3lvve5dvzu57xq

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence [article]

Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu
2018 arXiv   pre-print
The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services.  ...  To cope with these challenges, we propose a novel autoencoder-based model to learn the non-linear user-POI relations, namely SAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware  ...  This development enables the advent of Location-based Social Networks (LBSNs), such as Yelp and Foursquare.  ... 
arXiv:1809.10770v1 fatcat:4jbtumufrfbefc436kzqxufeku

A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

Xiaoyao Zheng, Yonglong Luo, Liping Sun, Qingying Yu, Ji Zhang, Siguang Chen
2021 Applied Sciences  
Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time.  ...  In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network.  ...  Data Availability Statement: The data presented in this study are available online from https:// ieee-dataport.org/documents/beijing-poi-datasets-geographical-coordinates-and-ratings accessed on 9 October  ... 
doi:10.3390/app112110497 fatcat:kbxkiumixbhp7bz32d4n6iduoi

Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information

Lina Yao, Quan Z. Sheng, Xianzhi Wang, Wei Emma Zhang, Yongrui Qin
2018 ACM Transactions on Internet Technology  
Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years.  ...  Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience.  ...  Typically, the authors in [27] incorporate location-awareness in a topic-based POI recommender system and propose a Topic and Location-aware probabilistic matrix factorization (TL-PMF) method. e method  ... 
doi:10.1145/3134438 fatcat:cqyd6inu7jemlnputrltmwvao4

Point-of-Interest Recommendation in Location Based Social Networks with Topic and Location Awareness [chapter]

Bin Liu, Hui Xiong
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
The wide spread use of location based social networks (LBSNs) has enabled the opportunities for better location based services through Point-of-Interest (POI) recommendation.  ...  In light of this difference, this paper proposes a topic and location aware POI recommender system by exploiting associated textual and context information.  ...  Also, it was supported in part by Natural Science Foundation of China (70890082, 71028002). Fi-nally, the author gratefully acknowledges the support of K. C. Wong Education Foundation, Hong Kong.  ... 
doi:10.1137/1.9781611972832.44 dblp:conf/sdm/LiuX13 fatcat:mmpwpj36araovcq2yfuugsws4u

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Areej Bin Suhaim, Jawad Berri
2021 IEEE Access  
Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years.  ...  In this research, we present a comprehensive review of context-aware recommender systems developed for social networks.  ...  Furthermore, modeling locationbased user behaviors in location-based social media network services through a context-aware regression mixture model is presented in [33] .  ... 
doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation [chapter]

Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani
2020 Lecture Notes in Computer Science  
With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs.  ...  However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences.  ...  [17] proposed a location neighborhood-aware weighted matrix factorization model to exploit the location perspective that incorporates the geographical relationships among POIs.  ... 
doi:10.1007/978-3-030-45439-5_14 fatcat:bllw4aurbzeaxig5jh44mpmgcu

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation [article]

Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani
2020 arXiv   pre-print
With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs.  ...  However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences.  ...  [17] proposed a location neighborhood-aware weighted matrix factorization model to exploit the location perspective that incorporates the geographical relationships among POIs.  ... 
arXiv:2001.08961v1 fatcat:xqwoxqlvlzclhgazni2d43eya4

Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling [article]

Jing Yang, Carsten Eickhoff
2018 arXiv   pre-print
In this work, we present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects  ...  For location recommendation, we propose a Spatio-Temporal Embedding Similarity algorithm (STES) based on the embedding model.  ...  ACKNOWLEDGMENTS We thank Zhiyuan Cheng for providing us with the check-in data set and Bo Hu for sharing their location recommendation algorithm details.  ... 
arXiv:1704.03507v2 fatcat:plycyogarja6xow2xa3wcdhejm
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