Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering

• 作者: Mengnan Qiu Xiaolan Xie
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: COLLABORATION FILTERING RECOMMENDATION algorithm LATENT Factor Model Cluster validity index SPECTRAL CLUSTERING
• 年，卷（期）: 2019,（1）
• 页码范围: 98-100
• 页数: 3页
• 分类号: C

摘要

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms.