A Novel Recommender Model Using Trust Based Networks

Butt, Muhammad Hassaan Farooq; Zhang, Xinyou; Khan, Ghufran Ahmad; Masood, A.; Butt, Muhammad Adnan Farooq; Khudayberdiev, Otabek

doi:10.1109/iccwamtip47768.2019.9067723

Cited by 3 publications

(3 citation statements)

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“…The first is the user-based collaborative filtering algorithm. The user-based collaborative filtering algorithm is to find similar users, and the Pearson correlation coefficient is used to calculate the similarity [1].…”

Section: Figure 3 Rmse Changes With Trust Propagation Value Bmentioning

confidence: 99%

“…Users may easily be recommended movies, books, music, and other items using recommendation technology. Collaboration-based recommendation approaches have been extensively researched in academic research [1][2][3][4][5][6] and are also widely applied in business industries. Well-known corporations like Amazon and eBay, for example, are examples of this success.…”

Section: Introductionmentioning

confidence: 99%

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Retracted: Collaborative learning‐assisted recommendation of social trust over Web of Things

Shrivastava

Saravanan²,

Rizwan

et al. 2022

IET Communications

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The above article from IET Communications, published online on 24 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Figure 3 Rmse Changes With Trust Propagation Value Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retracted: Collaborative learning‐assisted recommendation of social trust over Web of Things

Shrivastava

Saravanan²,

Rizwan

et al. 2022

IET Communications

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“…The IMP-GCN model [24] uses user features and graph structures to identify users with similar interests and recommends products to users with similar interests. Zhang et al [25] proposed model considers the new factor between active users and the nearest neighbor, introduces the trust network into the recommendation model, and selects the best trust path between users through algorithm integration, which improves recommendation performance.…”

Section: Related Workmentioning

confidence: 99%

In-depth Recommendation Model Based on Self-Attention Factorization

2023

KSII TIIS

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Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machines for rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.

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