Abstract：To alleviate the sparse data and cold start problem of cross-domain recommendation， a cross-domain recommendation model (DLLFM-DA/SelfAtten CDR Model (DLDASA)) is proposed to integrate the dual-tower hidden semantics and self-attention mechanism， which can effectively improve the target domain recommendation accuracy. First， it uses the proposed dual-tower hidden semantic model (DLLFM) to generate high-quality hidden semantics with the help of category preferences and item categories of users in the source and target domains； second， it introduces domain adaptation in the process of hidden semantic feature migration to effectively align the feature distribution between the source and target domains， minimize the difference of data distribution between the source and target domains， and provide higher quality hidden semantic feature migration. Then， the multi-headed self-attentiveness mechanism is used to capture the difference and correlation between two domains， and filter and fuse the difference information to alleviate the negative migration problem in order to improve the quality of cross-domain recommendation. Finally， the experimental results of comparing DLDASA with the baseline single-domain and cross-domain recommendation models on Movielens-Netflix and YPWK (YPWK)-ZBJW (ZBJW) real datasets show that both root mean square error (RMSE) and mean absolute error (MAE) are significantly improved. It is verified that the DLDASA model can more fully extract user features and effectively alleviate the problem of insufficient information in the target domain in this study.
操凤萍,张锐汀,窦万峰. 基于双塔隐语义与自注意力的跨域推荐模型[J]. 华中师范大学学报(自然科学版), 2023, 57(5): 724-732.
CAO Fengping,ZHANG Ruiting,DOU Wanfeng. Cross-domain recommendation model based on two-tower latent semantic and self-attention. journal1, 2023, 57(5): 724-732.
2023, Vol. 57 
