Collaborative Filtering Recommendation Algorithm Based on Similarity of Co-Rating Sequence

In order to improve the accuracy of the recommendation system, we study the influence of the number of co-rating items between users and the sequence associations between similar users on user ratings. When calculating the user similarity, we can not only consider the influence of user ratings, but also consider the similarity between user association sequences consisting of the number of co-rating items. And on this basis, we propose a more accurate user similarity measurement method, and get a more accurate user rating prediction method.The experimental results show that the proposed similarity calculation method combined with the co-rating sequence can more accurately characterize the user similarity, the user ratings prediction mean square error is smaller and the recommended effect is effectively improved compared with other algorithms. While the algorithm is based on a large number of experimental foundations, does not include deep learning into the category, so the choice of fusion coefficient may not be optimal.