Research on recommendation method based on tensor similarity

Abstract

Traditional recommendation methods model users as vectors in a way that focuses only on single-sided user preferences. In order to compensate for the limitations of this modelling approach, a tensor modelling method is proposed that models the user as a rectangle. Firstly, a recommendation model based on a fusion of collaborative filtering and sequential recommender algorithms is constructed, which integrates the Transformer model and Pooling layer to model the user tensor; secondly, the similarity between the user tensor and the target item is calculated by combining the distance between the user tensor and the target item and the bias. The model is experimentally validated on the MovieLens datasets, and the results show that the model is able to focus on multiple user preferences and outperforms the baseline method in terms of accuracy of recommendation results.