关键词: 协同过滤, k-means聚类, 用户偏好, 相似度, Weighted Slope One算法

Abstract: The collaborative filtering recommendation algorithm uses rating data as the data source for learning. Aiming at the sparse rating data and the scalability of the collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on clustering and user preferences is proposed. In order to mine users’ preferences, this algorithm introduces the average score of user to item type into the score matrix, and adds the similarity based on user’s own attributes. At the same time, in order to reduce data sparsity, the Weighed Slope One algorithm is used to fill the unrated items in the rating data, and the K-means algorithm based on density and distance optimization of initial clustering center is used to cluster the users in the filled rating data, which reduces the search space of similar users. Finally, the traditional collaborative filtering recommendation algorithm is used in the clustered data set to generate the recommendation results of the target users. By using the MovieLen100K dataset, the experiment shows that the paposed algorithm improves the recommendation effect.

Key words: collaborative filtering, k-means clustering, user preference, similarity, Weighted Slope One algorithm