SeqRec：基于长期偏好和即时兴趣的序列推荐模型

通过对用户的历史购物序列进行建模，得到用户稳定的长期偏好和动态的即时兴趣，并聚合长期偏好和即时兴趣进行个性化推荐. 提取用户对商品的评论内容用于表示商品的特征；使用递归神经网络从用户的历史购买序列数据中学习用户稳定的长期偏好，使用提问数据对用户不断变化的即时兴趣进行建模；通过注意力机制为长期偏好以及即时兴趣分配不同的权重，得到用户最终偏好的向量表示. 在亚马逊真实数据集上的实验结果表明，SeqRec模型在召回率和精确率上比当前主流的序列推荐方法至少超出10%；SeqRec模型证明不同用户的长期偏好和即时兴趣对其下次购买物品的影响程度不同.

SeqRec: sequential-based recommendation model with long-term preference and instant interest

The user’s stable long-term preferences and dynamic instant interests were obtained by modeling on the user’s historical behavior records, and the user preferences were aggregated for personalized recommendation. Firstly, the users’ reviews on the items were extracted to represent the characteristics of the items. Secondly, users’ historical behavior records were used to represent their stable long-term preferences, and query data was used to model their instant interests. Third, the user’s final preferences were aggregated by assigning different weights to the long-term preferences and instant interests through the attention mechanism. Experiments on real data sets of Amazon were conducted to evaluate the performance of SeqRec model, and results show that it is superior to the current state-of-the-art sequential recommendation methods more than 10% in recall rate and percision ratio. Meanwhile, SeqRec model proves that the long-term preferences and instant interests of different users have different influences on their next purchases.