基于艾宾浩斯遗忘曲线和注意力机制的推荐算法

doi:10.11959/j.issn.1000-0801.2022266

Abstract

Abstract:

Traditional attention-based recommendation algorithms only use position embeddings to model user behavior sequences, however, ignore specific timestamp information, resulting in poor recommendation performance and overfitting of model training.The multi-task matrix factorization recommendation model based on time attention was proposed, which used the attention mechanism to extract the neighborhood information for the user and item embedding, and used the Ebbinghaus forgetting curve to describe the changing characteristics of user interests over time.The model training process introduced a reinforcement learning strategy of experience replay to simulate the human memory review process.Experimental results on real datasets show that the proposed model has better recommendation performance than existing recommendation models.

Key words: Ebbinghaus forgetting curve, attention mechanism, reinforcement learning, experience replay

CLC Number:

TP391

Nan JIN, Ruiqin WANG, Yuecong LU. Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm[J]. Telecommunications Science, 2022, 38(10): 89-97.

Figures/Tables 11

References 22

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数据集	FilmTrust	MovieLens 100K	MovieLens 1M	MovieLens Last
用户数/位	1 508	943	6 040	610
电影数/部	2 071	1 682	3 706	9 742
评分数/个	35 497	100 000	1 000 209	100 836
评分范围	[0.5,4]	[1,5]	[1,5]	[1,5]
评分密度	1.14%	6.30%	4.47%	1.70%

模型	FilmTrust		MovieLens 100K		MovieLens 1M		MovieLens Last
模型	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
ItemCF	0.657 3	0.882 4	0.749 1	0.969 7	0.731 3	0.955 6	0.818 1	1.041 3
PMF	0.640 5	0.823 1	0.753 2	0.933 5	0.735 6	0.897 1	0.685 4	0.894 2
MLP	0.648 2	0.824 2	0.725 0	0.929 6	0.692 8	0.880 0	0.667 2	0.871 9
SVD	0.634 6	0.822 8	0.746 7	0.922 3	0.721 8	0.907 5	0.665 2	0.852 3
GCN	0.631 7	0.815 7	0.700 3	0.896 2	0.689 2	0.862 2	0.651 6	0.848 0
NeuMF	0.632 6	0.810 6	0.712 3	0.906 2	0.687 6	0.859 3	0.650 2	0.849 8
DeepCF	0.632 0	0.817 4	0.698 0	0.895 0	0.668 8	0.852 4	0.647 6	0.842 5
TAMMF	0.622 0	0.803 0	0.697 2	0.893 3	0.667 1	0.846 9	0.644 7	0.838 8
TAMMF+	0.614 3	0.798 8	0.694 9	0.888 9	0.662 8	0.845 6	0.631 8	0.832 2

数据集	性能	size=32	size=64	size=128	size=256
FilmTrust	RMSE	0.812 6	0.807 6	0.798 8	0.802 6
	MAE	0.623 3	0.619 3	0.614 3	0.617 3
ML-100K	RMSE	0.906 1	0.896 1	0.888 9	0.894 9
	MAE	0.708 1	0.698 9	0.694 9	0.698 2
ML-Last	RMSE	0.846 1	0.836 4	0.832 2	0.834 4
	MAE	0.656 3	0.636 1	0.631 8	0.639 1
ML-1M	RMSE	0.853 4	0.843 4	0.845 6	0.848 4
	MAE	0.681 0	0.669 0	0.662 8	0.666 0

Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm

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