Abstract：In this paper, a collaborative filtering recommendation algorithm based on user preference is proposed. First of all, the user
similarity is calculated according to the length of the longest common subsequence of different user interest sequences and the num-
ber of common subsequences, and then the similarity obtained by this algorithm is weighted and mixed with the similarity obtained
by traditional collaborative filtering recommendation algorithm. Project recommendation is completed based on mixed similarity
and the possible project score by target users is predicted. Finally, by comparing the average absolute error MAE values of three rec-
ommendation algorithms in three data sets of Ciao, Flixster and MovieLens 100K, it is proved that the proposed user collaborative
filtering recommendation algorithm (XQCF) has improved the accuracy of the recommendation system.
宋曼. 一种基于用户偏好的协同过滤推荐算法[J]. 电脑与电信, .
SONG Man. ACollaborative Filtering RecommendationAlgorithm Based on User Preference. Computer & Telecommunication, 2020, 1(12): 17-21.
