| Autor: |
Hong, Tiet Gia, Hoang Vy, Ho Thi, Thanh Ha, Do Thi, Kim Nhung, Ho Le Thi, My Hang, Vu Thi, Pham-Nguyen, Cuong, Hoai Nam, Le Nguyen |
| Předmět: |
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| Zdroj: |
Procedia Computer Science; 2024, Vol. 246, p2469-2478, 10p |
| Abstrakt: |
To provide personalized recommendations for users, recommendation systems must predict their unknown preferences. Latent factor models consistently achieve high prediction accuracy for this task. During training, these models encode users and items as latent vectors. Aligning these vectors facilitates predicting the item preference of the user. The training process optimizes the objective function, aiming to minimize the disparity between latent vectors and the collected preferences. In this study, we aim to integrate item descriptions into the construction of the objective function due to the sparsity and inaccuracy of collected preferences. This process is accomplished using Bert for vectorizing item descriptions. We conducted experiments with the proposed approach on datasets Movielens 1M and Yahoo Webscope R4. Our approach demonstrates a reduction in RMSE compared to previous approaches within this research domain. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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