基于场因子分解的xDeepFM推荐模型

doi:10.3969/j.issn.0255-8297.2024.03.012

Abstract

Abstract: The eXtreme deep factorization machine (xDeepFM) is a context-aware recommendation model integrating a compressed interaction network for controllable feature cross-ordering. The network is combined with deep neural network to optimize the recommendation performance. To further improve xDeepFM’s performance in recommended scenarios, eXtreme deep field factorization machine (xDeepFFM) is proposed in this paper. The improved model enhances feature expression capabilities through field information and uses multiple compressed interaction networks to learn higher-order combinatorial features based on field information. Furthermore, this paper analyzes the rationality of the setting of user field and item field. The effectiveness of the improved model is evaluated using area under curve and Log-likelihood loss metrics on three public datasets of different sizes.

Key words: recommendation algorithm, eXtreme deep factorization machine (xDeepFM), field factorization, deep learning

CLC Number:

TP391

LI Zijie, ZHANG Shu, OUYANG Zhaoxiang, WANG Jun, WU Di. xDeepFM Recommendation Model Based on Field Factorization[J]. Journal of Applied Sciences, 2024, 42(3): 513-524.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2024.03.012

https://www.jas.shu.edu.cn/EN/Y2024/V42/I3/513

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xDeepFM Recommendation Model Based on Field Factorization

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