融合用户行为序列预测的混合推荐算法

doi:10.16180/j.cnki.issn1007-7820.2023.04.012

Abstract

Abstract:

The capture of user interest hidden in the user behavior sequence is a hot research direction of recommendation algorithms in recent years. The traditional sequence prediction model uses the last product clicked by the user as the target, and establishes the association between user behavior and the target product, but does not fully dig out the sequence relationship between user sequences. This study improves on the traditional DIN model, uses continuous behavior over a period of time as the target vector, uses the transformer structure to complete the sequence-to-sequence prediction task, and further extracts and utilizes the user's deep interest in the user behavior sequence, and it is recommended in conjunction with DIN as an auxiliary feature. The experimental results on the Amazon book and the electronic data sets show that the DIN-based hybrid recommendation model proposed in this study increases the AUC index of the original DIN model by about 0.7% and 1.9%, respectively. It can be seen that the hybrid recommendation based on user behavior sequence prediction can play a certain auxiliary role in the multi-feature recommendation system. In addition, the influence of user sequence length on the model results is also explored.

Key words: recommendation system, click-through rate estimation, advertising calculation, CTR estimation, mixed recommendation, user sequence, user preference, attention mechanism

CLC Number:

TP391

SUN Hong,LU Meike. Hybrid Recommendation Algorithm Fused with User Behavior Sequence Prediction[J].Electronic Science and Technology, 2023, 36(4): 84-89.

Figures/Tables 8

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References 18

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模型参数	参数值
BatchSize	32
序列长度	15
多头注意力头数	2
Block	1
优化器	Adam

模型	AUC (books)	AUC (electronic)
BaseModel	0.708 32	0.677 24
DIN	0.748 45	0.709 13
DIEN	0.750 11	0.710 44
混合模型	0.753 67	0.722 39

Hybrid Recommendation Algorithm Fused with User Behavior Sequence Prediction

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