计算机应用 ›› 2016, Vol. 36 ›› Issue (2): 414-418.DOI: 10.11772/j.issn.1001-9081.2016.02.0414

• 第三届CCF大数据学术会议(CCF BigData 2015) • 上一篇    下一篇

基于内容的推荐与协同过滤融合的新闻推荐方法

杨武, 唐瑞, 卢玲   

  1. 重庆理工大学 计算机科学与工程学院, 重庆 400054
  • 收稿日期:2015-09-15 修回日期:2015-09-23 出版日期:2016-02-10 发布日期:2016-02-03
  • 通讯作者: 唐瑞(1990-),男,四川泸州人,硕士研究生,主要研究方向:推荐系统。
  • 作者简介:杨武(1965-),男,湖北武汉人,教授,博士,CCF会员,主要研究方向:信息检索;卢玲(1975-),女,河南焦作人,讲师,硕士,主要研究方向:信息检索、文本信息挖掘。
  • 基金资助:
    重庆市科委基础与前沿研究计划项目(cstc2014jcyjA40007);重庆市教委科学技术研究项目(KJ1500903)。

News recommendation method by fusion of content-based recommendation and collaborative filtering

YANG Wu, TANG Rui, LU Ling   

  1. College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
  • Received:2015-09-15 Revised:2015-09-23 Online:2016-02-10 Published:2016-02-03

摘要: 针对基于内容的新闻推荐方法中用户兴趣多样性的缺乏问题和混合推荐方法存在的冷启动问题,提出一种基于内容与协同过滤融合的方法进行新闻推荐。首先利用基于内容的方法发现用户既有兴趣;再用内容与行为的混合相似度模式,寻找目标用户的相似用户群,预测用户对特征词的兴趣度,发现用户潜在兴趣;然后将用户既有兴趣与潜在兴趣融合,得到兼具个性化和多样性的用户兴趣模型;最后将候选新闻与融合模型进行相似度计算,形成推荐列表。实验结果显示,与基于内容的推荐方法相比,所提方法的F-measure和整体多样性Diversity均有明显提高;与混合推荐方法相比,性能相当,但候选新闻无需耗时积累足够的用户点击量,不存在冷启动问题。

关键词: 新闻推荐, 协同过滤, 基于内容的推荐, 用户兴趣模型, 混合推荐

Abstract: To solve poor diversity problem of user interests in content-based news recommendation and cold-start problem in hybrid recommendation, a new method of news recommendation based on fusion of content-based recommendation and collaborative filtering was proposed. Firstly, the content-based method was used to find the user's interest. Secondly, similar user group of the target user was found out by using hybrid similarity pattern which contains content similarity and behavior similarity, and the user's potential interest was found by predicting the user's interest in feature words. Next, the user interest model with characteristics of personalization and diversity was obtained by fusing user's existed interest and potential interest. Lastly, the recommendation list was output after calculating the similarity of candidate news and fusion model. The experimental results show that, compared with the content-based recommendation methods, the proposed method obviously increases F-measure and Diversity; and it has equivalent performance with hybrid recommendation method, however it does not need time to accumulate enough user clicks of candidate news and has no cold start problem.

Key words: news recommendation, collaborative filtering, content-based recommendation, user interest model, hybrid recommendation

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