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Fold embedding and attention-based collaborative filtering with masking strategy for consumer products rating prediction

Jiaqi Liu, Jialong Jiang, Mingwei Lin, Hong Chen, Zeshui Xu

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 23 December 2024

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Abstract

Purpose

When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.

Design/methodology/approach

First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.

Findings

The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.

Originality/value

We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.

Keywords

Citation

Liu, J., Jiang, J., Lin, M., Chen, H. and Xu, Z. (2024), "Fold embedding and attention-based collaborative filtering with masking strategy for consumer products rating prediction", International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJICC-08-2024-0362

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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