• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

In-depth Recommendation Model Based on Self-Attention Factorization

Vol. 17, No. 3, March 31, 2023
10.3837/tiis.2023.03.003, Download Paper (Free):

Abstract

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. Thismodel 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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Cite this article

[IEEE Style]
H. Ma and Q. Liu, "In-depth Recommendation Model Based on Self-Attention Factorization," KSII Transactions on Internet and Information Systems, vol. 17, no. 3, pp. 721-739, 2023. DOI: 10.3837/tiis.2023.03.003.

[ACM Style]
Hongshuang Ma and Qicheng Liu. 2023. In-depth Recommendation Model Based on Self-Attention Factorization. KSII Transactions on Internet and Information Systems, 17, 3, (2023), 721-739. DOI: 10.3837/tiis.2023.03.003.

[BibTeX Style]
@article{tiis:38501, title="In-depth Recommendation Model Based on Self-Attention Factorization", author="Hongshuang Ma and Qicheng Liu and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2023.03.003}, volume={17}, number={3}, year="2023", month={March}, pages={721-739}}