基于项目聚类的全局最近邻的协同过滤算法

用户评分数据极端稀疏的情况下，传统相似性度量方法存在弊端，导致推荐系统的推荐质量急剧下降。针对 此问题，提出了一种基于项目聚类的全局最近部的协同过滤算法。该算法根据项目之间的相似性进行聚类，使得相似 性较高的项目聚成一类，在项目聚类集的基础上，计算用户的局部相似度，使用一种新的最近部用户全局相似度作为 衡量用户间相似性的标准;其次，给出了一种利用重叠度因子来调节局部相似度的方法，以更准确地刻画用户之间的 相似性。实验结果表明，该算法可以提升预测结果的准确性，提高推荐质量，特别是在数据较为稀疏时，改善尤为明 显。

Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity

Abstract When facing with the extreme sparsity of user rating data, traditional similarity measure method performs poor work which results in poor recommendation duality. To address the matter, a new collaborative filtering recommen- elation algorithm based on item clustering and global nearest neighbor set was proposed. Clustering algorithm is applied to cluster items into several classes based on the similarity of the items, and then the local user similarity is calculated in each cluster, at last a newly global similarity between nearest neighbor users is used to measure user similarity. In addi- tion,the factor of overlap is introduced to optimize the accuracy of the local similarity between users. The experimental results show that this algorithm can improve the accuracy of the prediction and enhance the recommendation quality, which shows good result on the condition of the extreme sparse data.