In order to solve the cold start problem of traditional recommendation algorithm, the sequence change of user interaction information and deep learning are gradually considered as a key feature of commodity recommendation system. However, most of the existing recommendation methods based on the sequence changes assume that all the interaction information of users is equally important for recommendation, which is not always applicable in real scenarios, because the interaction process of user items is full of randomness and contingency. In this article, we study how to reduce the randomness and contingency between session sequences, make full use of the association between session sequences in the interaction process of users by Deep Learning. In order to better simulate the change of session sequence in the real scene, we adopt sequence sampling methods to transform the single classification problem into sequence modeling problem. And attention mechanism is added to reduce the interference of the recommendation model in the sequence due to the contingency and randomness of the user in the shopping. Finally, through the verification of real data, the MRR@20 index of the improved model is 20% higher than the benchmark level.

中文翻译：

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基于深度学习的序列推荐

为了解决传统推荐算法的冷启动问题，用户交互信息的序列变化和深度学习逐渐被视为商品推荐系统的关键特征。然而，现有的基于序列变化的推荐方法大多假设用户的所有交互信息对推荐都同等重要，这在实际场景中并不总是适用，因为用户项目的交互过程充满了随机性和偶然性。在本文中，我们研究如何通过深度学习来降低会话序列之间的随机性和偶然性，在用户交互过程中充分利用会话序列之间的关联。为了更好地模拟真实场景中会话序列的变化，我们采用序列采样方法将单一分类问题转化为序列建模问题。并且加入了注意力机制，以减少由于用户在购物中的偶然性和随机性对序列中推荐模型的干扰。最后，通过真实数据的验证，改进模型的MRR@20指标比基准水平高出20%。