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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
Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews.  ...  To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works.  ...  Introduction Location-based Social Networks (LBSNs) offer users a unique opportunity to socialize by sharing their check-ins, opinions, photos, and reviews.  ... 
arXiv:2011.10187v1 fatcat:3uampnqerfdvnpuzrxcrsjviwq

Analyzing and inferring human real-life behavior through online social networks with social influence deep learning

Luca Luceri, Torsten Braun, Silvia Giordano
2019 Applied Network Science  
We validate and evaluate our approaches using data from Plancast, an Event-Based Social Network, and Foursquare, a Location-Based Social Network.  ...  We introduce Social Influence Deep Learning (SIDL), a framework that combines deep learning with network science for modeling social influence and predicting human behavior on real-world activities, such  ...  Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  ... 
doi:10.1007/s41109-019-0134-3 fatcat:3h4wm7t5crhnhed4s2fqrwhabu

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.  ...  To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree  ...  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 Central Universities in  ... 
arXiv:1909.12807v2 fatcat:2nj4crzcd5attidhd3kneszmki

Spatio-Temporal Attention based Recurrent Neural Network for Next Location Prediction

Basmah Altaf, Lu Yu, Xiangliang Zhang
2018 2018 IEEE International Conference on Big Data (Big Data)  
Experimental results on two real location based social networks Gowalla, and BrightKite show that our proposed method outperforms the existing state-of-the-art deep neural network methods for next POI  ...  Among them, the family of methods based on Markov chain can capture the instance-level interaction between a pair of POI checkins, while recurrent neural network (RNN) based approaches (stateof-the-art  ...  ., l M } in a location based social network (LBSN).  ... 
doi:10.1109/bigdata.2018.8622218 dblp:conf/bigdataconf/AltafYZ18 fatcat:vz7mgveg7zcwnfouwt7x3eoaha

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

Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing
2019 Complexity  
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  ...  network and a predefined location network, respectively.  ...  [38] adopted a network embedding method for the construction of social networks, it is proposed for friend recommendation and next location recommendation. Moreover, Yang et al.  ... 
doi:10.1155/2019/3574194 fatcat:yvdlqwr77jahlovqada6e2zs2e

Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

Aaron Ling Chi Yi, Dae-Ki Kang
2021 Applied Sciences  
However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users.  ...  In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user's social influence and also the native or local expert's  ...  Acknowledgments: The authors wish to thank members of the Dongseo University Machine Learning/Deep Learning Research Laboratory and the anonymous referees for their helpful comments on earlier drafts of  ... 
doi:10.3390/app11062510 fatcat:qq63pdogobc63au46xlbrnwfje

CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation [article]

Yi Tay, Anh Tuan Luu, Siu Cheung Hui
2018 arXiv   pre-print
Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users.  ...  In this paper, we present a text-based computational approach for estimating the relationship compatibility of two users on social media.  ...  . • RQ1 -How well are machine learning and deep learning methods able to learn, predict, recommend relationships just based on linguistic information from social profiles?  ... 
arXiv:1805.11535v1 fatcat:yyctnvrkrvewnc7km2i7tdegdi

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks [article]

Zirui Zhu, Chen Gao, Xu Chen, Nian Li, Depeng Jin, Yong Li
2021 arXiv   pre-print
We develop a Social HyperGraph Convolutional Network (short for SHGCN) to learn from the complex triplet social relations.  ...  Incorporating social relations into the recommendation system, i.e. social recommendation, has been widely studied in academic and industrial communities.  ...  A user can launch a groupbuying and invite his/her friends to join via sending URL links in social network.  ... 
arXiv:2111.03344v1 fatcat:jmqde6idvbc5now3doiz6poch4

RLINK: Deep reinforcement learning for user identity linkage

Xiaoxue Li, Yanan Cao, Qian Li, Yanmin Shang, Yangxi Li, Yanbing Liu, Guandong Xu
2020 World wide web (Bussum)  
Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based  ...  User identity linkage is a task of recognizing the identities of the same user across different social networks (SN).  ...  With the development of network embedding and deep learning, embedding based methods and deep learning models have been utilized to solve UIL problem.  ... 
doi:10.1007/s11280-020-00833-8 fatcat:svmepr4fprb47pzgz4rse442s4

Group-Buying Recommendation for Social E-Commerce [article]

Jun Zhang, Chen Gao, Depeng Jin, Yong Li
2020 arXiv   pre-print
In this new business model, users, initiator, can launch a group and share products to their social networks, and when there are enough friends, participants, join it, the deal is clinched.  ...  In this work, we take the first step to approach the problem of group-buying recommendation for social e-commerce and develop a GBGCN method (short for Group-Buying Graph Convolutional Network).  ...  Our GBGCN approaches the problem via graph convolutional network-based representation learning on the graph-structured group-buying data.  ... 
arXiv:2010.06848v2 fatcat:ewdu4gbz7zed3pwfzd3mybqwee

Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

Mengyue Hang, Ian Pytlarz, Jennifer Neville
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied.  ...  We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).  ...  Also since check-ins to location-based social networks are often sporadic [10] , it can be difficult to identify consistent user patterns.  ... 
doi:10.1145/3219819.3219902 dblp:conf/kdd/HangPN18 fatcat:otcygqdcerffhlzagaxgtm23qe

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

Areej Bin Suhaim, Jawad Berri
2021 IEEE Access  
Her research interests include recommender system, information retrieval, social network, data mining and machine learning.  ...  Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years.  ...  [93] recommend friends for a user by applying a deep learning technique.  ... 
doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

RLINK: Deep Reinforcement Learning for User Identity Linkage [article]

Xiaoxue Li, Yanan Cao, Yanmin Shang, Yangxi Li, Yanbing Liu, Jianlong Tan
2019 arXiv   pre-print
Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based  ...  User identity linkage is a task of recognizing the identities of the same user across different social networks (SN).  ...  Fig. 2A Procedure of Reinforcement Learning based User Identity Linkage. The blue link represents friend relation in social network, and orange line at S i represents matched identity pair.  ... 
arXiv:1910.14273v1 fatcat:rgxhurl4lved5nybf22ymgsbwm

Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach

Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux
2019 The World Wide Web Conference on - WWW '19  
Location Based Social Networks (LBSNs) have been widely used as a primary data source to study the impact of mobility and social relationships on each other.  ...  Based on this hypergraph, we first propose a random-walk-with-stay scheme to jointly sample user check-ins and social relationships, and then learn node embeddings from the sampled (hyper)edges by preserving  ...  Specifically, after learning the node embeddings based on the old social network and check-ins, we rank pairs of user nodes (not being friends in the old social network) according to the cosine similarity  ... 
doi:10.1145/3308558.3313635 dblp:conf/www/YangQYC19 fatcat:iap6zqo6tzgt3p563xmfrzv7fy

LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks

Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux
2020 IEEE Transactions on Knowledge and Data Engineering  
Location-Based Social Networks (LBSNs) have been widely used as a primary data source for studying the impact of mobility and social relationships on each other.  ...  Based on this hypergraph, we first propose a random-walk-with-stay scheme to jointly sample user check-ins and social relationships, and then learn node embeddings from the sampled (hyper)edges by not  ...  Specifically, after learning the node embeddings based on the old social network and check-ins, we rank pairs of user nodes (not being friends in the old social network) according to the cosine similarity  ... 
doi:10.1109/tkde.2020.2997869 fatcat:m5zamr37rzectgo5lyciuvupry
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