协同过滤的相似度融合改进算法

针对传统协同过滤推荐在数据稀疏性条件下性能不佳的问题,在相似度计算上做出了优化,提出了一种基于项目类别和用户兴趣相似度融合的协同过滤算法,算法将相似度的计算分解为两个方面进行:用户-项目类别评分相似度和用户-项目类别兴趣相似度,将两者用合适的权值加以融合得到最终相似度,参与最终预测评分的计算.利用MovieLens公用数据集对改进前后的算法进行对比.结果表明,基于项目类别和用户兴趣的协同过滤改进算法有效地缓解了数据稀疏性问题的影响,提高了推荐的准确性.

Improved Collaborative Filtering Algorithm of Similarity Integration

Aiming at the poor recommendation quality due to the data sparsity problem of traditional collaborative filtering recommendation, this paper puts forward an improved collaborative filtering algorithm.The improved algorithm proposes a collaborative filtering algorithm based on the similarity integration of item categories and user interests to make optimization on the similarity calculation.The algorithm does not simply concentrate on similarity calculation, but divides it into two aspects:users-item category interest similarity and users-item category rating similarity, which will finally be integrated with appropriate weights to get the final similarity.After a series of verification and comparison carried out on the MovieLens public data set, it is concluded that the improved algorithm based on data sparsity of collaborative filtering indeed plays a positive role in reducing the influence caused by data sparsity and improves the accuracy of recommendation.