计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 36-41.

• 智能计算 • 上一篇    下一篇

基于用户阅读时间-频次行为的书籍推荐方法

曹斌,龚佼蓉,彭宏杰,赵立为,范菁   

  1. 浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61173097),浙江省重大科技专项重大工业项目(2013C01112),杭州市重大科技创新专项(20132011A16)资助

Recommending Books Based on Reading Time and Frequency

CAO Bin, GONG Jiao-rong, PENG Hong-jie, ZHAO Li-wei and FAN Jing   

  • Online:2018-11-14 Published:2018-11-14

摘要: 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。

关键词: 协同过滤,推荐系统,用户评分矩阵,用户行为,时间-频次

Abstract: 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.

Key words: Collaborative filtering,Recommendation system,User ratings matrix,User behavior,Time-frequency

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