推荐系统中分布式混合协同过滤方法

传统协同过滤方法面临数据稀疏问题，稀疏的用户-项目关联数据将产生不准确的相似用户或项目，为了改善推荐质量，提出一种基于Map Reduce的混合协同过滤方法.该方法利用用户特征和用户-项目评分数据构造项目偏好向量，然后使用模糊K-Means算法对项目进行聚类，并从每个项目簇中选择相似项目，最后组合所有项目簇的预测结果作出推荐.实验结果显示，该方法能缓解数据稀疏问题，改善推荐精度.

A Distributed Hybrid Collaborative Filtering Method in Recommender Systems

Addressing the information overloading problem, the collaborative filtering is an effective tech-nique, and extensively applied in recommender systems. It make predictions by finding users with similar taste or items that have been similarly chosen. However, as the number of users or items grows rapidly, the traditional collaborative filtering approach is suffering from the data sparsity problem. The sparse user-item associations can generate inaccurate neighborhood for each user or item. A distributed hybrid collab-orative filtering method was proposed based on Map Reduce, aiming at improving the recommendation quality. This method utilizes user features and ratings to construct item preference vectors. Then, it clus-ters items using fuzzy K-Means algorithm, and respectively chooses similar items from each clustering , fi-nally it combines all predictions from each clustering and makes recommendation. Experiments show that the distributed hybrid collaborative filtering method can help reduce the sparsity problem, and improve the recommendation accuracy.