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A comparative study of heterogeneous item recommendations in social systems

Alejandro Bellogín, Iván Cantador, Pablo Castells
2013 Information Sciences  
We present a comparative study on the influence that different types of information available in social systems have on item recommendation.  ...  in social systems is valuable for effective item recommendations.  ...  Acknowledgments The work presented here was supported by the Spanish Ministry of Science and Innovation (TIN2011-28538-C02), and the Autonomous Community of Madrid (CCG10-UAM/TIC-5877).  ... 
doi:10.1016/j.ins.2012.09.039 fatcat:houxbiojzvc4pdtq3kjjllrexm

A study of heterogeneity in recommendations for a social music service

Alejandro Bellogín, Iván Cantador, Pablo Castells
2010 Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems - HetRec '10  
We present a preliminarily study on the influence of different sources of information in Web 2.0 systems on recommendation.  ...  The obtained results show that, in, social tagging and explicit social networking information provide effective and heterogeneous item recommendations.  ...  FUTURE WORK We have presented a preliminary study on the influence of heterogeneous sources of information in Web 2.0 systems on recommendation.  ... 
doi:10.1145/1869446.1869447 fatcat:o5xusuzo3jgjfpj7557wwmwlze

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
2021 arXiv   pre-print
In this survey, we review the development of recommendation frameworks with the focus on heterogeneous relational learning, which consists of different types of dependencies among users and items.  ...  Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload.  ...  In a typical recommendation scenario, we have a set of users u i ∈ U and items v j ∈ V with the size of I and J, respectively.  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

H2Rec: Homogeneous and Heterogeneous Network Embedding Fusion for Social Recommendation

Yabin Shao, Cheng Liu
2021 International Journal of Computational Intelligence Systems  
A B S T R A C T Due to the problems of data sparsity and cold start in traditional recommendation systems, social information is introduced.  ...  At present, most social recommendation is based on the homogeneity or heterogeneity of social networks.  ...  Although recommender systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems has just started.  ... 
doi:10.2991/ijcis.d.210406.001 fatcat:3tvxhnn4vffmzffvegnbskc7uy

Heterogeneous Social Recommendation Model with Network Embedding

Chang Su, Zongchao Hu, Xianzhong Xie
2020 IEEE Access  
As a result of social relations is always sparse and weak for specific item, therefore, to better integrate social relations into the recommendation system, we propose a model named heterogeneous social  ...  In the social recommendation system, all kinds of social relations are intricate. HIN has been used in the recommendation system to extract rich auxiliary information.  ... 
doi:10.1109/access.2020.3038022 fatcat:xcapp4isljeanbjyqp6vwqrms4

Hybrid Recommendation in Heterogeneous Networks [chapter]

Robin Burke, Fatemeh Vahedian, Bamshad Mobasher
2014 Lecture Notes in Computer Science  
Compared to prior work with shorter metapaths, we show that a hybrid composed of components using longer metapaths yields improvements in recommendation diversity without loss of accuracy on social tagging  ...  In this paper, we present a general approach to recommendation in heterogeneous networks that can incorporate multiple relations in a weighted hybrid.  ...  Resource recommendation is the task of identifying items of interest for a user in social tagging system based on tagging behavior.  ... 
doi:10.1007/978-3-319-08786-3_5 fatcat:fa2wwssvkja53k3crnoay2bczu

Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation [article]

Chuan Shi, Jian Liu, Fuzhen Zhuang, Philip S. Yu, Bin Wu
2015 arXiv   pre-print
In this paper, we organize objects and relations in recommendation system as a heterogeneous information network, and introduce meta path based similarity measure to evaluate the similarity of users or  ...  Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance.  ...  Furthermore, they presented a comparative study on the influence that different types of information available in social systems have on item recommendation [2] .  ... 
arXiv:1511.03759v1 fatcat:nlx76g3revdeviz6r2o7brvpye

Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation [article]

Kyung-Min Kim, Donghyun Kwak, Hanock Kwak, Young-Jin Park, Sangkwon Sim, Jae-Han Cho, Minkyu Kim, Jihun Kwon, Nako Sung, Jung-Woo Ha
2019 arXiv   pre-print
HGP uses a group-user-item tripartite graph as input to reduce the number of edges and the complexity of paths in a social graph.  ...  The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well. Here we propose a new graph embedding method Heterogeneous Graph Propagation (HGP) to tackle these issues.  ...  CONCLUSION In this paper, we proposed a graph configuration, group-user-item tripartite attributed multiplex heterogeneous networks, for a social recommender system.  ... 
arXiv:1908.02569v1 fatcat:br7cbjrybzat7o7sr2zuvryf5y

Recommendation in heterogeneous information network via dual similarity regularization

Jing Zheng, Jian Liu, Chuan Shi, Fuzhen Zhuang, Jingzhi Li, Bin Wu
2016 International Journal of Data Science and Analytics  
The social recommendation methods tend to leverage social relations among users obtained from social network to alleviate data sparsity and cold-start problems in recommender systems.  ...  Recommender system has caught much attention from multiple disciplines, and many techniques are proposed to build it. Recently, social recommendation becomes a hot research direction.  ...  Study on cold-start problem Cold start is an important issue in recommender system.  ... 
doi:10.1007/s41060-016-0031-0 dblp:journals/ijdsa/ZhengLSZLW17 fatcat:o2wce62dyrhxxmd7xbbqpfaq5m

Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation

Fengli Xu, Jianxun Lian, Zhenyu Han, Yong Li, Yujian Xu, Xing Xie
2019 Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19  
Recent years have witnessed a phenomenal success of agent-initiated social e-commerce models, which encourage users to become selling agents to promote items through their social connections.  ...  The complex interactions in this type of social e-commerce can be formulated as Heterogeneous Information Networks (HIN), where there are numerous types of relations between three types of nodes, i.e.,  ...  CONCLUSION In this paper, we study the recommender system design in an emerging scenario, i.e., social e-commerce.  ... 
doi:10.1145/3357384.3357924 dblp:conf/cikm/XuLHLX019 fatcat:wgznfbekajeg5kezix2dgjeecm

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Rongmei Zhao, Xi Xiong, Xia Zu, Shenggen Ju, Zhongzhi Li, Binyong Li
2020 Complexity  
In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only  ...  It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps.  ...  Related Works In this section, we will introduce the study of the traditional recommender system, trust recommender system, and graph convolution recommender system, respectively. is paper builds on the  ... 
doi:10.1155/2020/9071624 fatcat:akl2qqzoyrbhbogjhhx7gpbl5a

Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation [article]

Gediminas Adomavicius and Dietmar Jannach and Stephan Leitner and Jingjing Zhang
2021 arXiv   pre-print
In this paper, we discuss how Agent-Based Modeling and Simulation (ABM) techniques can be used to study such important longitudinal dynamics of recommender systems.  ...  Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users  ...  ABM has been successfully applied in a variety of research fields, including Social Sciences, Economics, and Information Systems, to model complex adaptive systems.  ... 
arXiv:2108.11068v1 fatcat:pbi6oxnwp5amhnhxsi6omrs6ny

BRS cS: a hybrid recommendation model fusing multi-source heterogeneous data

Zhenyan Ji, Chun Yang, Huihui Wang, José Enrique Armendáriz-iñigo, Marta Arce-Urriza
2020 EURASIP Journal on Wireless Communications and Networking  
Recommendation systems are often used to solve the problem of information overload on the Internet.  ...  It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-N recommendations.  ...  Using deep learning to fuse multiple heterogeneous data in the data source layer to improve the accuracy of the recommendation results is still worth studying [31] [32] [33] [34] .  ... 
doi:10.1186/s13638-020-01716-2 fatcat:xqjvxo25wrbptpvx2rdsnsnfze

Non-IIDness Learning in Behavioral and Social Data

L. Cao
2013 Computer journal  
These aspects are embodied in behavioral and social systems in terms of specific corresponding entities and attributes. For instance, from the hierarchical perspective, a social system may  ...  Case studies, related work and prospects of non-IIDness learning are presented. Non-IIDness learning is also a fundamental issue in big data analytics.  ...  ACKNOWLEDGEMENTS This work is sponsored in part by Australian Research Council Discovery Grants (DP1096218 and DP130102691) and ARC Linkage Grant (LP100200774).  ... 
doi:10.1093/comjnl/bxt084 fatcat:rrivwnak7jgxfmjdgdiexi4meq

Dual Similarity Regularization for Recommendation [chapter]

Jing Zheng, Jian Liu, Chuan Shi, Fuzhen Zhuang, Jingzhi Li, Bin Wu
2016 Lecture Notes in Computer Science  
Recently, social recommendation becomes a hot research direction, which leverages social relations among users to alleviate data sparsity and cold-start problems in recommender systems.  ...  In order to overcome the shortcomings of social regularization, we propose a new dual similarity regularization to impose the constraint on users and items with high and low similarities simultaneously  ...  Inspired by the success of Heterogeneous Information Network (HIN) in many applications, we organize objects and relations in a recommender system as a HIN, which can integrate all kinds of information  ... 
doi:10.1007/978-3-319-31750-2_43 fatcat:hxcbsvtufjfabpgiqfcf43pccy
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