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

Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing
2019 Complexity  
As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years.  ...  Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based POI recommendation method is proposed.  ...  model for learning the representations of POIs. e definition of the POI-POI graph is shown as follows.  ... 
doi:10.1155/2019/3574194 fatcat:yvdlqwr77jahlovqada6e2zs2e

Learning Semantic Relationships of Geographical Areas based on Trajectories

Saim Mehmood, Manos Papagelis
2020 2020 21st IEEE International Conference on Mobile Data Management (MDM)  
The methods we present are generic and can be utilized to inform a number of useful applications, ranging from location-based services, such as point-of-interest recommendations, to finding semantic relationships  ...  First, we present a method that utilizes trajectories to learn low-dimensional representations of geographical areas in an embedded space.  ...  The method relies on random-walk based methods for learning node representation of a graph and is able to reveal latent relationships of geographical areas, effectively defining semantic relationships  ... 
doi:10.1109/mdm48529.2020.00032 dblp:conf/mdm/MehmoodP20 fatcat:qewjnpcydncrlekfhxqauz5r3y

Mining Business Opportunities from Location-based Social Networks

Shenglin Zhao, Irwin King, Michael R. Lyu, Jia Zeng, Mingxuan Yuan
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
for a specific business district based on the learned category embedding representations.  ...  To improve user experience and prosper the businesses in LBSNs, a variety of new applications come out, e.g., point-of-interest (POI) recommendation and retail allocation system.  ... 
doi:10.1145/3077136.3080712 dblp:conf/sigir/ZhaoKLZY17 fatcat:upctykcuezhohotqz7dl3q3paa

Relation Embedding for Personalised Translation-Based POI Recommendation [chapter]

Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz
2020 Lecture Notes in Computer Science  
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services.  ...  To this end, we propose a translation-based relation embedding for POI recommendation.  ...  We acknowledge the support of Australian Research Council Discovery DP190101485, Alexander von Humboldt Foundation, and CSIRO Data61 Scholarship program.  ... 
doi:10.1007/978-3-030-47426-3_5 fatcat:zron5negfbea5lqfgnmz6rimuq

Relation Embedding for Personalised POI Recommendation [article]

Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz
2020 arXiv   pre-print
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services.  ...  To this end, we propose a translation-based relation embedding for POI recommendation.  ...  Acknowledgments We acknowledge the support of Australian Research Council Discovery DP190101485, Alexander von Humboldt Foundation, and CSIRO Data61 Scholarship program.  ... 
arXiv:2002.03461v2 fatcat:uscrnbl6qrdela3moehxtes7tq

DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation [article]

Liwei Huang, Yutao Ma, Yanbo Liu, Keqing He
2020 arXiv   pre-print
Next (or successive) point-of-interest (POI) recommendation has attracted increasing attention in recent years.  ...  In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR.  ...  Ma is the corresponding author of this paper.  ... 
arXiv:2004.12161v1 fatcat:7ymnb4z4kndbrfkjwy35sy67aq

POI neural-rec model via graph embedding representation

Kang Yang, Jinghua Zhu, Xu Guo
2021 Tsinghua Science and Technology  
With the booming of the Internet of Things (IoT) and the speedy advancement of Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting  ...  Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the  ...  GE [32] : It is a POI recommendation model based on graph embedding, which jointly embeds bipartite graphs of POIs, time, regions, and behaviors in the latent space.  ... 
doi:10.26599/tst.2019.9010059 fatcat:rbshihct5bh2dkb3dzpwqhm4du

GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network

Shiwen Wu, Yuanxing Zhang, Chengliang Gao, Kaigui Bian, Bin Cui
2020 Data Science and Engineering  
One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate pointof-interest (POI) recommendation based on users' interests.  ...  To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of  ...  HRec [26] leverages social relationship to enhance user representations and adopts graph learning approach to learn the user/POI representations from three graphs.  ... 
doi:10.1007/s41019-020-00135-z fatcat:qlo77xrwdzbknfvqco5qjxwcdy

RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings [article]

Giannis Christoforidis, Pavlos Kefalas, Apostolos N. Papadopoulos and Yannis Manolopoulos
2019 arXiv   pre-print
The exploitation of Points-of-Interest (POIs) recommendation by existing models is inadequate due to the sparsity and the cold start problems.  ...  More specifically, RELINE captures: i) the social, ii) the geographical, iii) the temporal influence, and iv) the users' preference dynamics, by embedding eight relational graphs into one shared latent  ...  Learning users' history is a crucial task for these models, to provide meaningful suggestions for Points-of-Interest (POIs).  ... 
arXiv:1902.00773v1 fatcat:cdvnjwvzsfh3ra3fp6rndb4to4

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations [article]

Md. Ashraful Islam, Mir Mahathir Mohammad, Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali
2020 arXiv   pre-print
Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation.  ...  To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works.  ...  [123] proposed Graph-based Latent Representation model (GLR) which can capture geographical influence, temporal influence, user preference, etc.  ... 
arXiv:2011.10187v1 fatcat:3uampnqerfdvnpuzrxcrsjviwq

Learning Spatiotemporal-Aware Representation for POI Recommendation [article]

Bei Liu, Tieyun Qian, Bing Liu, Liang Hong, Zhenni You, Yuxiang Li
2017 arXiv   pre-print
Current studies on representation learning for POI recommendation embed both users and POIs in a common latent space, and users' preference is inferred based on the distance/similarity between a user and  ...  The wide spread of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs).  ...  Users on LBSN like to share their experiences with their friends for points of interest (POIs), e.g., restaurants and museums.  ... 
arXiv:1704.08853v1 fatcat:adswz5qstbckxh25bicuqsnwk4

Research Commentary on Recommendations with Side Information: A Survey and Research Directions [article]

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
2019 arXiv   pre-print
One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models.  ...  The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video  ...  We also gratefully acknowledge the support of National Natural Science Foundation of China (Grant No. 71601104, 71601116, 71771141 and 61702084) and the support of the Fundamental Research Funds for the  ... 
arXiv:1909.12807v2 fatcat:2nj4crzcd5attidhd3kneszmki

Linking OpenStreetMap with Knowledge Graphs – Link Discovery for Schema-Agnostic Volunteered Geographic Information [article]

Nicolas Tempelmeier, Elena Demidova
2020 arXiv   pre-print
OSM2KG adopts this latent representation to train a supervised model for link prediction and utilises existing links between OSM and knowledge graphs for training.  ...  Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete.  ...  OSM2KG learns this latent node representation automatically from OSM tags.  ... 
arXiv:2011.05841v1 fatcat:pqbvtczkovaplnb7mtoo5nczyu

A Survey of Context-Aware Recommendation Schemes in Event-Based Social Networks

Xiaomei Huang, Guoqiong Liao, Naixue Xiong, Athanasios V. Vasilakos, Tianming Lan
2020 Electronics  
Finally, we point out research opportunities for the research community.  ...  To provide better service for users, Context-Aware Recommender Systems (CARS) in EBSNs have recently been singled out as a fascinating area of research.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics9101583 fatcat:hdapa7y4vfcefcprlngjnjn5ai

Linked Open Data in Location-Based Recommendation System on Tourism Domain: a survey

Phatpicha Yochum, Liang Chang, Tianlong Gu, Manli Zhu
2020 IEEE Access  
for reacting to the needs of tourists.  ...  Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.  ...  They proposed a representation learning method, named Joint Representation Learning Model (JRLM) to model check-in sequences with social connections, and produced a latent representation for user and location  ... 
doi:10.1109/access.2020.2967120 fatcat:yqwkrko6mzfw5e5kckfaxbxzju
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