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Learning to recommend with social trust ensemble

Hao Ma, Irwin King, Michael R. Lyu
2009 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09  
In this framework, we coin the term Social Trust Ensemble to represent the formulation of the social trust restrictions on the recommender systems.  ...  The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method performs  ...  This work is also affiliated with the Microsoft-CUHK Joint Laboratory for Human-centric Computing and Interface Technologies.  ... 
doi:10.1145/1571941.1571978 dblp:conf/sigir/MaKL09 fatcat:nf4lfnr3tveyloedv3qgc4avru

Learning to Recommend with Hidden Factor Models and Social Trust Ensemble

Dan Zhao, Junyi Wang, Andi Gao, Pengfei Yue
2015 Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication   unpublished
As one of the most successful approaches to building recommender systems, Collaborative Filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown  ...  This paper introduces some innovations to both approaches. The factor, topic and trust models can now be smoothly merged, to build a more accurate combined model.  ...  'Reco mmendation with Social Trust Ensemble' (RSTE).  ... 
doi:10.2991/csic-15.2015.20 fatcat:koxd5t4h7fhhhmiwizv2y7hkjm

Review of Social Collaborative Filtering Recommender System's Methods

Pratibha Yadav
2016 International Journal Of Engineering And Computer Science  
Recommender Systems plays a vital role in e-commerce. The goal of recommender system is to present the user with the personalized information that matches with the user's interest.  ...  Social Networking Sites provide users a platform to connect and share their information with other users who share similar interests with user.  ...  Trust Ensemble Model Social Trust Ensemble Model (STEM) is the combination of MF and social network based approach [28] .  ... 
doi:10.18535/ijecs/v4i10.49 fatcat:roo2xsud3fhmrhca7wjeawcy7y

Review of Social Collaborative Filtering Recommender System's Methods

Pratibha Yadav
2016 International Journal Of Engineering And Computer Science  
Recommender Systems plays a vital role in e-commerce. The goal of recommender system is to present the user with the personalized information that matches with the user's interest.  ...  Social Networking Sites provide users a platform to connect and share their information with other users who share similar interests with user.  ...  Trust Ensemble Model Social Trust Ensemble Model (STEM) is the combination of MF and social network based approach [28] .  ... 
doi:10.18535/ijecs/v4i11.06 fatcat:4id6npgcvfdhfo4i7if4y7k56i

Deep Learning based Trust-Aware Recommender for Social Networks

Pooja Dinkar Shinde
2019 International Journal for Research in Applied Science and Engineering Technology  
In this paper, we propose a new recommendation technique for the trust aware recommendation in social networks based on the Deep Learning (DL).  ...  The community detection algorithm based on trust relations in social networks is proposed for the revamp the MF (Matrix Factorization) model with social trust together and community effect.  ...  CONCLUSION AND FUTURE SCOPE In this paper, we present trust aware recommendation in social networks based on Deep Learning (DL).  ... 
doi:10.22214/ijraset.2019.6380 fatcat:l4zzplq7fjekbpg4s7vhikdgmi

Collaborative Deep Forest Learning for Recommender Systems

Soheila Molaei, Amirhossein Havvaei, Hadi Zare, Mahdi Jalili
2021 IEEE Access  
Here, we propose an end-to-end deep learning framework by learning latent social features to embed in a CF approach.  ...  INDEX TERMS Recommender systems, social networks, deep learning, collaborative filtering, representational learning.  ...  Davoudi and Chatterjee [34] dealt with the issue of modeling social trust where trust values dominated the characteristics of users in a social network.  ... 
doi:10.1109/access.2021.3054818 fatcat:gvyjzdcgbna37l2mb5pzyy65yu

Social Recommendation in Dynamic Networks [chapter]

Hao Ma, Irwin King, Michael R. Lyu
2014 Encyclopedia of Social Network Analysis and Mining  
For example, in users' social trust network, users tend to share their similar interests with the friends they trust.  ...  Social Trust Ensemble However, the above algorithm does not consider any information from users' social network.  ... 
doi:10.1007/978-1-4614-6170-8_189 fatcat:dq3zt5l6xrg4rl7vcchhp7uoqa

The Method of Personalized Recommendation with Ensemble Combination

Jiwan Seo, Seungjin Choi, Mucheol Kim, Sangyong Han
2013 Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications  
It can be applied to not only the personalized recommendations but also the detection of malicious insider users which attack with unfair rating.  ...  In this paper, we propose a method in which the trust and reputation models are harmonized through an ensemble combination.  ...  This paper proposes the ensemble combination method with trust and reputation. It can be utilized to recommend movies and also treat the attacks which occur by malicious users.  ... 
doi:10.22667/jowua.2013.12.31.108 dblp:journals/jowua/SeoCKH13 fatcat:vfa65yqjuzdcpdixqlf3kox6qi

Network Representation Learning Enhanced Recommendation Algorithm

Qiang Wang, Yonghong Yu, Haiyan Gao, Li Zhang, Yang Cao, Lin Mao, Kaiqi Dou, Wenye Ni
2019 IEEE Access  
With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms.  ...  Socialnetwork-based recommendation algorithms generally assume that users with trust relations usually share common interests.  ...  Social-network-based recommendation algorithms generally assume that users with trust relations usually share common interests.  ... 
doi:10.1109/access.2019.2916186 fatcat:vngczvs4czg4jgrbocsmbarjke

A Trust-aware Neural Collaborative Filtering for E-learning Recommendation

Xiaoyi Deng, Hailin Li, Feifei Huangfu
2018 Educational Sciences: Theory & Practice  
the performance of e-learning recommendation that aim to mitigate information overload and provide users with the most attractive and relevant learning resources.  ...  Social networks can provide massive quantities of information for communication among users and e-learning communities, and the trust relationships can been employed to reveal users' preferences for improving  ...  trust ensemble (STE) and social regularization (SR).  ... 
doi:10.12738/estp.2018.5.121 fatcat:y2gmhq6ymngilcv4fpl5jmag6a

A Brief Literature Survey on: Online Product Purchasing on User Behavior

Monika Pal
2017 International Journal of Computer Applications  
The applicability of trust to recommender systems has been established in several research studies.  ...  RESEARCH OBJECTIVE In this work we propose a trust based recommendation system by incorporating the idea of social networking.  ... 
doi:10.5120/ijca2017915266 fatcat:m3s264f37fajlbe4oipkg4hg4i

SoRank

Weilong Yao, Jing He, Guangyan Huang, Yanchun Zhang
2014 Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion  
Most existing learning to rank based recommendation methods only use user-item preferences to rank items, while neglecting social relations among users.  ...  In addition, with linear complexity to the number of observed ratings, SoRank is able to scale to very large dataset.  ...  SORANK In this section, we introduce the listwise learning to rank with social information for item recommendation.  ... 
doi:10.1145/2567948.2577333 dblp:conf/www/YaoHHZ14 fatcat:bjfs32kp7jfalhxxb4ri2hjiku

Learning to recommend with explicit and implicit social relations

Hao Ma, Irwin King, Michael R. Lyu
2011 ACM Transactions on Intelligent Systems and Technology  
R. 20011. learning to recommend with explicit and implicit social relations.  ...  In this framework, we coin the term social trust ensemble to represent the formulation of the social trust restrictions on the recommender systems.  ...  ACKNOWLEDGMENTS The authors would like to thank the reviewers and editor for their helpful comments.  ... 
doi:10.1145/1961189.1961201 fatcat:4kiodm7kurbnvacmvmexfil5sa

Socially Enabled Preference Learning from Implicit Feedback Data [chapter]

Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, Stéphane Canu
2013 Lecture Notes in Computer Science  
The advent of online social networks has added another approach to recommendation whereby the social network itself is used as a source for recommendations i.e. users are recommended items that are preferred  ...  In this paper we develop a new model-based recommendation method that merges collaborative and social approaches and utilizes implicit feedback and the social graph data.  ...  We will denote this method as Trust Ensemble. We also compare SECoFi to a baseline : the average predictor, which will recommend the overall most popular places to each user.  ... 
doi:10.1007/978-3-642-40991-2_10 fatcat:6v26gy3wofbbde6k47i4fxc5ye

Social recommender approach for technology-enhanced learning

Mohammed Tadlaoui, Karim Sehaba, Sébastien George, Azeddine Chikh, Karim Bouamrane
2018 International Journal of Learning Technology  
The ensemble recommender system Ensemble uses the two existing forms of social navigation, namely collaborative filtering and history-enriched information spaces to guide users.  ...  Attitudes Overall, I am satisfied with the recommender. I am convinced of the resources recommended to me. I am confident I will like the items recommended to me. The recommender can be trusted.  ... 
doi:10.1504/ijlt.2018.091631 fatcat:43hnr2lwpfac5l4fkwzcb63o6m
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