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Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks
2019
Complexity
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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
2020
2020 21st IEEE International Conference on Mobile Data Management (MDM)
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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
2017
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17
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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]
2020
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2021
Tsinghua Science and Technology
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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
2020
Data Science and Engineering
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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]
2019
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2020
Electronics
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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
2020
IEEE Access
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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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