Journal Article10.1007/s00500-022-07440-x
A comprehensive social matrix factorization for recommendations with prediction and feedback mechanisms by fusing trust relationships and social tags
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TL;DR: A social recommendation method incorporating trust relationships and social tags is proposed, which obtains user similarity and item similarity through potential feature vectors of users and items, and continuously trains them to obtain accurate similarity relationships to improve recommendation performance.
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About: This article is published in Soft Computing. The article was published on 29 Aug 2022. The article focuses on the topics: Computer science & RSS.
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Citations
Collaborative filtering recommendations based on multi-factor random walks
TL;DR: Wang et al. as discussed by the authors proposed a collaborative filter recommendation based on multi-factor random walk to solve the problem of data sparsity by using trust relationships to improve the accuracy of recommendation.
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Joint friend and item recommendation based on multidimensional feature reciprocal interaction in social e-commerce
Wei Zhou,Feipeng Guo,Huijian Xu,Z Wang +3 more
References
•Proceedings Article
Probabilistic Matrix Factorization
Andriy Mnih,Ruslan Salakhutdinov +1 more
- 03 Dec 2007
TL;DR: The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.
A matrix factorization technique with trust propagation for recommendation in social networks
Mohsen Jamali,Martin Ester +1 more
- 26 Sep 2010
TL;DR: A model-based approach for recommendation in social networks, employing matrix factorization techniques and incorporating the mechanism of trust propagation into the model demonstrates that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
SoRec: social recommendation using probabilistic matrix factorization
Hao Ma,Haixuan Yang,Michael R. Lyu,Irwin King +3 more
- 26 Oct 2008
TL;DR: A factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records is proposed.
Learning to recommend with social trust ensemble
Hao Ma,Irwin King,Michael R. Lyu +2 more
- 19 Jul 2009
TL;DR: This work proposes a novel probabilistic factor analysis framework, which naturally fuses the users' tastes and their trusted friends' favors together and coin the term Social Trust Ensemble to represent the formulation of the social trust restrictions on the recommender systems.
Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
TL;DR: In this paper, the Human-Centered Artificial Intelligence (HCAI) is proposed as a well-designed technology that combines human control and computer automation to increase human performance, leading to wider adoption.