基于生成对抗模型的序列推荐算法

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Abstract Existing recommendation algorithms use item similarity or user similarity to recommend items without capturing the sequence pattern of user interaction with items. In fact, the interaction sequence between users and items contains important context information, which is of great significance for generating user interaction predictions. In this paper, a sequence recommendation algorithm based on a generative adversarial model is proposed. First, a convolutional neural network is used as a generator to capture the sequence pattern of the user interaction sequence, and then the attention mechanism is used as the discriminator of the generative adversarial networks to capture the time information of the sequence and the item content attribute information. We also use an improved time embedding method to model the time periodic change of the interaction. The generative adversarial networks simultaneously model the user's long-term preferences and short-term preferences. Experiments on the public data sets MovieLens-1M and Amazon-Beauty prove that the proposed algorithm has a significant improvement over all baseline methods according to HR@N and NDCG@N.

Key words： sequence recommendation convolutional neural network attention mechanism

Received: 05 June 2021

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	Articles by authors
	CHEN Jiwei
	WANG Haitao
	JIANG Ying
	CHEN Xing

Cite this article:

CHEN Jiwei,WANG Haitao,JIANG Ying, et al. Sequential Recommendation with Generative Adversarial Networks[J]. , 2022, 36(7): 143-153.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I7/143

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