【摘要】 为了解决传统协同过滤算法的冷启动问题,提高算法的推荐质量,本文针对协同过滤算法中的冷启动问题进行研究,提出了两种改进的算法.新用户冷启动:融合用户信息模型的基于用户的协同过滤算法;新项目冷启动:采用层次聚类的基于项目的协同过滤算法.将新算法在网络开源数据集MovieLens上进行实验验证,比较改进算法和传统算法在查全率和查准率上的差异,结果表明改进算法能够有效地提高算法的推荐质量,缓解新用户和新项目的冷启动问题.更多还原

【Abstract】 In order to solve the cold-start problem of the traditional collaborative filtering algorithm and to improve the performance of recommendation, this study focuses on the cold-start problem and proposes two algorithms. Cold-start problem of new users: user-based collaborative filtering algorithm integrated with user’s information model, cold-start problem of new items: item-based collaborative filtering algorithm applying hierarchical clustering. After a series of experiments carried out on public data sets—MovieLens, comparing the difference between the precision and recall value of the improved algorithm and the traditional one, the results show that the new algorithm can effectively alleviate the cold start problem and improve the quality of recommendation.更多还原