基于用户评分一致性的协同过滤个性化推荐算法

Collaborative filtering personalized recommendation algorithm based on user scoring consistency

在目前信息高速发展的时代,个性化推荐作为信息过滤的重要手段,是解决信息超载的最有效方法之一。协同过滤一直是解决个性化推荐比较热门的技术,其主要思想是计算用户之间的相似性或计算项目之间的相似性,然后根据用户或项目之间的相似性对目标用户进行推荐。文章基于协同过滤的思想,提出了一种结合用户评分一致性的单模投影算法,首先在用户与项目的关系二部图中计算用户之间的评分一致性,然后把一致性赋值作为压缩之后的单模投影权值,最后用K近邻找到相似用户并做出个性化推荐。在MovieLens、FilmTrust和Jester等真实数据集上的实验表明,基于评分一致性的推荐算法达到了较好的效果。

In the era of rapid development of information,personalized recommendation,as an important means of information filtering,is one of the most effective ways to solve information overload.Collaborative filtering has always been a popular technology to solve personalized recommendation.Its main idea is to calculate the similarity between users or calculate the similarity between projects,and then recommend to the target users according to the similarity between users or projects.Based on the idea of collaborative filtering,this paper proposes a single-mode projection algorithm combined with user scoring consistency.Firstly,the scoring consistency between users is calculated in the relationship bipartite graph formed by users and projects.Then the consistency assignment is taken as the weight of the compressed single-mode projection.Finally,the method of K-nearest neighbor is used to find similar users and to make personalized recommendations.Experiments on real datasets such as MovieLens,FilmTrust and Jester show that the recommendation algorithm based on score consistency achieves better results.