The key to cross-domain recommendation is how to effectively map user features from source domain to target domain.Previous studies focused more on mapping user features,ignored the similarity between user features and actual features after mapping and the possibility of item features mapping;it also only focused on a single task.Therefore,a multitask features mapping network model for cross-domain recommendation(MTFMN)is proposed to address the above issues.The model first introduces a user features mapping network,which can map the user features from source domain to target domain.It also introduces an item features mapping network to assist the learning of the user features mapping network.In the process of optimizing,Euclidean distance is used to measure the similarity between user and item features before and after mapping,as an optimization strategy in the process of training.Finally,preference prediction task and rating prediction task are performed on the user features obtained through user features mapping network and the actual item features in target domain.On the Amazon datasets and Douban datasets,MTFMN has significantly improved the accuracy of rating prediction tasks compared to mainstream models.In addition,the model also conducts ablation studies to prove the effectiveness of the item features mapping network and multitask optimization strategy proposed in the model.
维普
万方数据