针对传统序列推荐算法时间信息和项目内容信息运用不充分的问题,该文提出基于生成对抗模型的序列推荐算法。通过生成对抗模型将序列建模与时间、内容信息建模分离,充分挖掘用户项目交互的序列信息和项目内容信息。运用卷积神经网络作为生成对抗模型的生成器,捕获用户项目交互的序列模式。运用注意力机制作为生成对抗模型的判别器,捕获交互序列的时间信息和项目内容信息。针对传统序列推荐算法时间信息建模不充分的问题,提出一种改进的时间嵌入方式,充分建模用户项目交互关于时间的周期性模式。利用生成对抗模型同时建模用户的稳定偏好和动态偏好,提升推荐系统的用户体验,并在公开数据集MovieLens-1M和Amazon-Beauty上与现有的优秀算法做比较。实验证明,该文所提出的算法在评价指标HR@N和NDCG@N上较基线方法均有一定提升。
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.
关键词
序列推荐 /
卷积神经网络 /
注意力机制
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Key words
sequence recommendation /
convolutional neural network /
attention mechanism
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参考文献
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基金
国家自然科学基金(61462049)
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