结合用户兴趣度聚类的协同过滤推荐算法

针对传统的协同过滤算法忽略了用户兴趣源于关键词以及数据稀疏的问题，提出了结合用户兴趣度聚类的协同过滤推荐算法。利用用户对项目的评分，并从项目属性中提取关键词，提出了一种新的RF-IIF （rating frequency-inverse item frequency）算法，根据目标用户对某关键词的评分频率和该关键词被所有用户的评分频率，得到用户对关键词的偏好，形成用户—关键词偏好矩阵，并在该矩阵基础上进行聚类。然后利用logistic函数得到用户对项目的兴趣度，明确用户爱好，在类簇中寻找目标用户的相似用户，提取邻居爱好的前◢N◣个物品对用户进行推荐。实验结果表明，算法准确率始终优于传统算法，对用户爱好判断较为准确，缓解了数据稀疏问题，有效提高了推荐的准确率和效率。

Collaborative filtering recommendation algorithm combined with user interest degree clustering

Aiming at the problem of ignores the user''s interest in the key words and the data sparseness in traditional collaborative filtering algorithm. We proposed a collaborative filtering recommendation algorithm combined with the user interest degree clustering. We used user ratings for projects and extracting keywords from item attributes. A new Rating Frequency-Inverse Item Frequency algorithm was proposed. According to the target users'' scoring frequency for a key word and the frequency of the keyword being evaluated by all users. We got users'' preferences for keywords, formed user preference matrix, and clustered on the basis of this matrix. Then we used logistic function to get users'' interest in projects. Cleared user preferences and found similar users of target users in the clusters. Then extracted N items from neighbors'' preferences, and recommended users. Experimental results show that the algorithm accuracy rate is always better than the traditional algorithm. it, s more accurate to judge the user interest, alleviating the problem of data sparseness, and effectively improves the accuracy and efficiency of recommendation.