基于用户兴趣变化的高校图书馆个性化图书推荐研究

在众多个性化推荐技术中，协同过滤算法作为一种适用范围广、推荐质量高的算法，在电子商务领域得到了广泛应用，近年来被不少学者引入到图书馆个性化推荐的研究中。然而高校图书馆系统与商业系统相比，普遍存在用户信息少、项目评分严重缺失的问题，极大地影响了个性化推荐的准确率。针对以上问题，文章从高校读者属性和行为特征出发，建立用户兴趣模型，将读者阅读兴趣划分为长期兴趣和短期兴趣，基于用户兴趣度优化项目评分矩阵，进行协同过滤图书推荐。

Research on Individualization Recommendation for University Library Based on the Change of User's Interest

Among the many individualization recommendation technologies, collaborative filtering with the merit of wide applicability and high quality has been widely used in e-commerce areas. Some scholars also attempted to apply this method in university libraries. However, compared with business system, university library system existed the problems, such as insufficient user information and lack of rating data, which severely impacted the quality of recommendation. In allusion to these problems, a model including both long-term and short-term reading interests of user was created based on user’s characteristics and behavior, which could be used to optimize rating matrix and improve book recommendation quality of collaborative filtering.