融合类别信息和用户兴趣度的协同过滤推荐算法

协同过滤直接根据用户的行为记录去预测其可能感兴趣的项目,是现今最成功、应用最广泛的推荐技术。推荐的准确度受相似性度量方法效果的影响。传统的相似性度量方法主要关注用户共同评分项之间的相似度,忽视了评分项目中的类别信息,在面对数据稀疏性问题时存在一定的不足。针对上述问题,提出基于分类信息 的评分矩阵填充方法,结合用户兴趣相似度计算方法并充分考虑到评分项目的类别信息,使得兴趣度的度量更加符合推荐系统应用的实际情况。实验结果表明,该算法可以弥补传统相似性度量方法的不足,缓解评分数据稀疏对协同过滤算法的影响,能够提高推荐的准确性、多样性和新颖性。

Collaborative Filtering Recommendation Algorithm Combing Category Information and User Interests

Collaborative filtering is the most successful and widely used information technology to make personalized prediction by exploiting the historical behaviors of users.The accuracy of the recommendation depends on effectiveness of the similarity measure.The methods of traditional similarity measure,which mainly concern with the similarity of the common ratings but ignore the category information in the rated items,are suffering from data sparsity problem.To address this issue,we proposed a ratings matrix filling method which is based on classification information by combining with user interest similarity calculation method and consider the category information fully to make the measure of interest more realistic.The experimental results show that the proposed algorithm can relieve the influence of the sparsity of user-item ratings on collaborative filtering algorithm and improve recommendation accuracy,diversity,and novelty.