基于用户阅读时间-频次行为的书籍推荐方法

Recommending Books Based on Reading Time and Frequency

随着电子阅读在近年来的兴起,通过研究用户对电子书籍的喜好,利用协同过滤推荐算法向用户进行个性化的书籍推荐具有实际应用价值,也成为了推荐系统研究中的重要内容。但当前很多书籍推荐应用中都存在缺少用户评分数据甚至没有用户评分的情况,使得传统协同过滤推荐方法的应用受阻。为解决此问题,通过分析处理用户阅读数据的相关行为数据,将此类行为数据通过时间-频次模型建模并得到用户-书籍评分矩阵,并利用该评分进一步实现基于用户的协同过滤书籍推荐算法。实验结果表明,改进的书籍协同过滤推荐算法的时间-频次模型能够提高书籍的推荐效果,具有实践研究意义。

With the rise of electronic reading in recent years,the use of collaborative filtering（CF） recommendation algorithm to recommend user personalized books has practical application value, and has become the important research content in the study of recommender systems. But many current e-reading systems for book recommendations lack of users＇ rating data,which hinders the application of CF. To address this,we analyzed the massive users＇ reading beha- vior,and proposed a reading time-frequency（T-F） model to profile the users＇ interests to the book. Thus, the implicit ratings matrix can be derived from this model and then classical CF algorithm could be used in a natural way. The experimental results show that the user based CF with our proposed T-F rating model can improve the recommendation effectiveness,which is feasible for real scenarios.