| dc.description.abstract | With the popularization of the Internet, we can easily find information on the Internet, such as movies, books and so on. However, the information explosion makes it difficult to find the information we want, accurately. For example, when two different users search for the same keywords, they will get the same results. Therefore, how to make recommendations based on users′ interests and users’ past behaviors becomes an important thing. Collaborative filtering (CF) is a mature and widely used technology in recommendation system. However, collaborative filtering also has some fatal problems, such as cold start. In this paper, we combine User-based CF with item-based CF with attention mechanism, so that our ACCF model can consider both of them simultaneously, so that when making recommendation prediction, more information can be referred to. Besides, the attention mechanism can automatically adjust the weight of the two models, thus reducing the negative impact of cold start. In addition, considering that traditional collaborative filtering cannot deal with the evolution of users′ interests, the model ACCF in this paper takes the evolution of users′ interests into consideration. The experimental results show that ACCF′s recommendation performance is better than other recommendation algorithms. In addition, we have also conducted experiments on several real datasets, proving that ACCF performs better than other collaborative filtering-based recommendation systems. | en_US |