Cross-Domain Recommendation Algorithm Combining Multi-personalized Bridges and Self-supervised Learning

Abstract

A cross-domain recommendation algorithm combining multi-personalized bridges and self-supervised learning (MS-PTUPCDR) is proposed for users with less project interaction in the target domain in the cross-domain recommendation system. Firstly, a variational bipartite graph encoder is added to the target domain, and a variational inference framework is used to generate potential variables. The target domain user representation aggregates their isomor-phic neighbor information. Secondly, the user??s single preference bridge is extended to the user??s multi-personalized preference bridge, the user's transferable user factors in the multi-source domain are transferred to the target domain, and the multi-head attention mechanism is added to the target domain to fuse the user's potential factors from different source domains as the auxiliary task of self-supervised learning. Finally, this paper aggregates user neighbor factors and the fused user multi-source domain transfer user factors for self-supervised learning. In the target domain, the project score of the target domain is predicted by the dot product of the user factor and the project factor of the target domain after the user's supervised learning. The algorithm is tested on two datasets, Amazon and MovieLens, and the results show that the algorithm outperforms the comparative baseline algorithm in terms of MAE and RMSE evaluation metrics. Compared with the optimal comparative baseline algorithm on both datasets, the MAE is improved by 1.96% on average, and the RMSE is improved by 1.92% on average, which verifies the effectiveness of the algorithm.

Keywords

• cross-domain recommendation; multi-personalized preference bridges for users; multi-head attention mechanism; self-supervised learning; variational bipartite graph encoder