Multi-temporal Sequential Recommendation Model Based on the Fused Learning Preferences

Abstract

Abstract Since sequential recommendation can effectively capture the user’s dynamic interaction pattern according to the user’s historical interaction, it plays one of the important roles in the field of recommendation systems. Nowadays, the sequential recommendation models based on deep learning approaches have achieved good results in reality due to their excellent performance in information retrieval and filtering. However, there are still some challenges that still should be overcome of them. A major disadvantage of them is that they are only considering users’ short-time interests while ignoring their long-term preferences. Moreover, they are incapable of considering the influence of time information in the original interaction sequence, which could be helpful to fully extract various patterns via the different temporal embedding forms. Therefore, this paper proposes a novel model named multi-temporal sequential recommendation model based on the fused learning preference named MTSR-FLP in short. In the proposed framework, this paper adopts an MLP-based state generation module to consider the user’s long-term preferences and the short-time interests simultaneously. In particular, the proposed MTSR-FLP model designs a global representation learning approach to obtain the user’s global preference, and a local representation learning approach to capture the users’ local preference via its historical information. Moreover, the proposed model develops a multiple temporal embedding scheme to encode the positions of user–item interactions of a sequence, in which multiple kernels are utilized for the absolute or relative timestamps to establish unique embedding matrices. Finally, compared with other advanced sequence recommendation models on five public real-world datasets, the experimental results show that the proposed MTSR-FLP model has improved the performance of HR@10 from the 6.68% through 31.10% and NDCG@10 from the 8.60% through 42.54%.

Keywords

• Sequential recommendation
• Deep learning
• Time series embedding
• Global representation learning
• Local representation learning