Recommendation Fusion Model Based on LSTM and Deep Matrix Factorization
Aiming at the problem that most recommendation algorithms in real recommendation scenarios ignore the timeliness factors of dy-namic changes in user preferences,resulting in limited model performance,a recommendation fusion model based on LSTM and deep matrix factorization(Long Short-Term Memory Fusion Deep Matrix Factorization,LFDMF)is proposed.The model uses generalized matrix factoriza-tion to learn nonlinear low-order features between users and items,uses multi-layer perceptron to learn nonlinear high-order features between users and items,and obtains users' long-term dynamic preferences.LSTM's strong fitting ability to time series is used to obtain users' short-term dynamic preferences.In order to verify the effectiveness and feasibility of the LFDMF model,comparative experiments are carried out on the public datasets MovieLens-1M and Pinterest.The simulation results show that the HR@10 and NDCG@10 indexes of the LFDMF model are improved by 0.103 4 and 0.132 2,0.118 1 and 0.101 8,respectively,compared with the traditional MF algorithm.Compared with the DMF model,it is improved by 0.022 8 and 0.032 3,0.016 9 and 0.013 5,respectively,the recommendation performance is significantly improved.
万方数据
维普
