SeqRec:基于长期偏好和即时兴趣的序列推荐模型

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.