基于模型填充的混合协同过滤算法

Hybrid Collaborative Filtering Recommendation Algorithm Based on Model Filling

随着商品与服务等领域电子海量数据的日益膨胀,协同过滤已成为一种新的推荐技术在推荐系统中倍受关注。本文研究分析了协同过滤中存在的数据极端稀疏、相似准确度问题,提出了一种基于模型填充的混合协同过滤算法。实验结果表明,该算法能有效解决用户评分数据的极端稀疏问题,可提高协同过滤的相似准确度。

As the Electronic data of goods and services expanding every day, collaborative filtering （CF） has become a popular and attractive technique in recommender systems. In this paper, a hybrid approach is proposed to solve problems which are challenges of the collaborative filtering, such as data sparsity, accurate of similarities. The experimental results show that our hybrid method can efficiently improve the extreme sparsity of user rating data, and improve the accurate of similarities in some extent.