基于用户聚类与项目划分的优化推荐算法

针对传统协同过滤推荐算法没有充分考虑用户属性及项目类别划分等因素对相似度计算产生的影响，存在数据稀疏性，从而导致推荐准确度不高的问题.提出一种基于用户属性聚类与项目划分的协同过滤推荐算法，算法对推荐准确度有重要影响的相似度计算进行了充分考虑.先对用户采用聚类算法以用户身份属性聚类，进而再对项目进行类别划分，在相似度计算中增加类别相似度，考虑共同评分用户数通过加权系数进行综合相似度计算，最后结合平均相似度，采用阈值法综合得出最近邻.实验结果表明，所提算法能够有效提高推荐精度，为用户提供更准确的推荐项目.

Optimal Recommendation Algorithm Based on User Clustering and Project Partition

The traditional collaborative filtering recommendation algorithm does not fully consider the impact of user attributes and item classification on similarity calculation, which results in data sparsity and low recommendation accuracy. This study proposes a collaborative filtering recommendation algorithm based on user attribute clustering and item partitioning. The algorithm fully considers the similarity calculation which has an important impact on recommendation accuracy. Firstly, users are clustered by user identity attributes using clustering algorithm, and then the items are classified. In the similarity calculation, category similarity is added. Considering the number of users scored jointly, comprehensive similarity is calculated by weighted coefficient. Finally, combined with average similarity, the nearest neighbor is synthesized by threshold method. The experimental results show that the proposed algorithm can effectively improve the recommendation accuracy and provide more accurate items for users.