Comparison of AFM with traditional FM/NFM and deep exploration of AFM: considering temperature parameters and attention size

Weidong Zhang

doi:10.1117/12.3011760

7 December 2023 Comparison of AFM with traditional FM/NFM and deep exploration of AFM: considering temperature parameters and attention size

Weidong Zhang

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294135 (2023) https://doi.org/10.1117/12.3011760
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Recommendation systems as one of the main products of today's Internet companies, as well as in view of the large number of current recommendation algorithms used in various mobile platforms. This paper focuses on the horizontal and vertical exploration of the model of the popular recommendation algorithm AFM. A cross-sectional comparison is made with two other recommendation models, FM and NFM, to demonstrate that AFM is superior in terms of some model evaluation metrics. In addition, the AFM recommendation model is improved by setting different temperature parameters and different feature interaction degrees to demonstrate the performance of AFM in terms of recommendation accuracy in different dimensions.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weidong Zhang "Comparison of AFM with traditional FM/NFM and deep exploration of AFM: considering temperature parameters and attention size", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294135 (7 December 2023); https://doi.org/10.1117/12.3011760

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