计算机科学 ›› 2018, Vol. 45 ›› Issue (4): 247-251.doi: 10.11896/j.issn.1002-137X.2018.04.041

• 人工智能 • 上一篇    下一篇

基于标签聚类与项目主题的协同过滤推荐算法

李昊阳,符云清   

  1. 重庆大学软件学院 重庆401331,重庆大学软件学院 重庆401331
  • 出版日期:2018-04-15 发布日期:2018-05-11

Collaborative Filtering Recommendation Algorithm Based on Tag Clustering and Item Topic

LI Hao-yang and FU Yun-qing   

  • Online:2018-04-15 Published:2018-05-11

摘要: 传统基于项目的协同过滤算法在计算项目相似度时仅依靠评分数据,未考虑项目的自身特征。社会化标注的出现使得标签能在一定程度上反映项目特征,但标签具有语义模糊的特点,因此直接将标签纳入协同过滤算法存在一定问题。为解决上述问题,提出一种改进的基于项目的协同过滤推荐算法。该算法对标签进行聚类并生成主题标签簇,根据项目标注情况计算项目与主题间的相关度并生成项目-主题相关度矩阵,同时将其与项目-评分矩阵相结合来计算项目间的相似度,采用协同过滤完成对目标项目的评分预测,以实现个性化推荐。在Movielens数据集上的实验结果表明,该算法能够解决标签的语义模糊问题并提升推荐质量。

关键词: 社会化标注,标签聚类,项目主题,协同过滤,个性化推荐

Abstract: The traditional item-based collaborative filtering algorithm only focuses on the rating data without the chara-cteristics of items when calculating the similarity between items.The appearance of social tagging can reflect the characteristics of items,but there are some semantic fuzziness problems while adding the social tags into the collaborative filtering algorithm directly.To solve the problems above,this paper put forward an improved item-based collaborative filtering recommendation algorithm.It clusters social tags to generate tag clusters which represent different topics,and calculates the relevance between items and topics to generate item-topics matrix according to the tagging results of items.The similarity between items is calculated by combining item-topics matrix with item-ratings matrix,the rating of target items are predicted through the collaborative filtering algorithm,and the personalized recommendation is realized.Expe-rimental results on MovieLens dataset show that the proposed algorithm can eliminate the semantic fuzziness and improve the quality of recommendation.

Key words: Social tagging,Tag clustering,Item topics,Collaborative filtering,Personalized recommendation

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