Abstract: The recommendation system is the core to solve the problem of information overload.The existing research on recommendation framework faces many problems,such as sparse explicit feedback data and difficulty to preprocess data,especially the recommendation performances for new users and new projects need to be further improved.With the advancement of deep lear-ning,recommendation based on deep learning has become a current research hotspot.A large number of experiments have proved the effectiveness of deep learning applied to recommendation system.This paper presents EANCF (Neural Collaborative Filtering based on Enhanced-attention Mechanism) on the basis of NCF.It studies the recommendation framework from the perspective of implicit feedback data,and considers the data feature extraction by means of max-pooling,local inference modeling and combining many different ways of data fusion.Meanwhile,attention mechanism is introduced to reasonably allocate weight value for the network,reduce the loss of information and improve the performance of recommendation.Finally,based on two large real data sets,Movielens-1m and Pinterest-20,comparative experiments are carried out between EANCF and NCF,as well as some classical algorithms,and the training process of EANCF framework is given in detail.The experimental results show that the proposed EANCF framework does have good recommendation performance.Compared with the NCF,both HR@10 and NDCG@10 are significantly improved,with the highest increase of 3.53% for HR@10 and 2.47% for NDCG@10.

Key words: Deep learning, Collaborative filtering, Implicit feedback, Attention mechanism