In order to overcome the sparse data and cold start of traditional collaborative filtering recommendation algorithm, a collaborative filtering algorithm based on co-clustering and C-RA combined similarity is proposed.First, co-clustering algorithm is used to simultaneously obtain user and item neighborhoods.Secondly, the result of co-clustering is used on rating matrix.Finally, C-RA combined similarity is used to calculate the similarity of users and recommend.Experimental results show that the proposed method not only effectively improves the accuracy of the recommended results, but also solves problems of user cold start and data sparsity.%针对传统协同过滤算法由于数据稀疏和冷启动而造成的推荐精度下降的问题,提出一种基于联合聚类和C-RA组合相似度的协同过滤算法.首先,通过联合聚类对原始评分矩阵进行用户和物品两个维度的聚类;其次,利用联合聚类结果填充原始评分矩阵;最后,利用C-RA组合相似度计算用户相似度并进行推荐.实验结果表明,该方法有效地提高了推荐结果的精确度,缓解了数据稀疏和冷启动问题.
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