融合用户特征优化聚类的协同过滤算法

针对推荐系统领域中应用最广泛的协同过滤推荐算法仍伴随着数据稀疏性、冷启动和扩展性问题,基于用户冷启动和扩展性问题,提出了基于改进聚类的PCEDS(pearson correlation coefficient and euclidean distance similarity)协同过滤推荐算法。首先针对用户属性特征,采用优化的K-means聚类算法对其聚类,然后结合基于信任度的用户属性特征相似度模型和用户偏好相似度模型,形成一种新颖的PCEDS相似度模型,对聚类结果建立预测模型。实验结果表明:提出的PCEDS算法比传统的协同过滤推荐算法在均方根误差(RMSE)上降低5%左右,并且推荐准确率(precision)和召回率(recall)均有明显提高,缓解了冷启动问题,同时聚类技术可以节省系统内存计算空间,从而提高了推荐效率。

Collaborative filtering algorithm combining user features and preferences in optimized clustering

The collaborative filtering recommendation algorithm in the field of recommendation systems is still accompanied by the data sparsity, cold start, and scalability problems. To solve the cold start and scalability problems, we propose a PCEDS(pearson correlation coefficient and euclidean distance) collaborative filtering recommendation algorithm based on optimized clustering. First, the optimized K-means clustering algorithm is used to cluster the attributes of users. Then, based on the trust-based similarity model of user attribute features and the similarity model of user preference, a novel PCEDS similarity model is established to create a prediction model for the clustering results. The experimental results indicate that, compared with the traditional collaborative filtering recommendation algorithm, the proposed PCEDS collaborative filtering recommendation algorithm reduces the root mean square error by approximately 5%, significantly improves the recommendation precision and recall, and solves the cold start problem. Simultaneously, the clustering technology can save the memory space of the recommendation system, thereby improving its efficiency.