Sequential recommendation based on contrast enhanced time-aware self-attention mechanism - YU Yang; WANG Ruiqin (Dianxin kexue)
The existing sequence recommendation models have shortcomings in utilizing absolute interaction time, resulting in inaccurate modeling of user preferences. Sequential recommendation based on contrast enhanced time-aware self-attention mechanism (CTiSASRec) was proposed. Firstly, the calculation process of attention weights integrated rating data, absolute interaction time, location information, and project popularity. Secondly, the absolute interaction time and location order of the project were integrated to generate a new project location embedding. Finally, during the training process, contrast learning based on the results of two modeling sequences was used to distinguish the similarities and differences between samples, thereby improving the accuracy and robustness of the model. Experimental studies conducted on six datasets of different fields and scales show that CTiSASRec outperforms state-of-the-art sequential recommendation models.