ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (5): 968-976.doi: 10.7544/issn1000-1239.2018.20170253

• 人工智能 • 上一篇    下一篇

一种基于社区专家信息的协同过滤推荐算法

张凯涵,梁吉业,赵兴旺,王智强   

  1. (山西大学计算机与信息技术学院 太原 030006) (计算智能与中文信息处理教育部重点实验室(山西大学) 太原 030006) (752750403@qq.com)
  • 出版日期: 2018-05-01
  • 基金资助: 
    国家自然科学基金项目(61432011,U1435212,61603230);山西省自然科学基金项目(201601D202039)

A Collaborative Filtering Recommendation Algorithm Based on Information of Community Experts

Zhang Kaihan, Liang Jiye, Zhao Xingwang,Wang Zhiqiang   

  1. (School of Computer and Information Technology, Shanxi University, Taiyuan 030006) (Key Laboratory of Computational Intelligence and Chinese Information Processing(Shanxi University), Ministry of Education, Taiyuan 030006)
  • Online: 2018-05-01

摘要: 协同过滤推荐算法由于不受特定领域知识限制、简单易实现等优点,得到了广泛的应用.但是,在实际应用中,该类算法往往面临着数据稀疏性、可扩展性、冷启动等问题.为了解决其中的用户冷启动问题,将用户社交信息和评分信息进行融合,提出了一种基于社区专家信息的协同过滤推荐算法.首先,依据用户的社交关系将用户划分为不同的社区;其次,根据一定的准则确定各个社区的专家,并利用社交信息和评分信息对专家评分进行填充进而缓解稀疏性;最后,对冷启动用户根据其所属社区的专家信息进行预测评分.在数据集FilmTrust和Epinions上与已有协同过滤推荐算法进行了比较分析.实验结果表明,提出的算法可以有效缓解协同过滤推荐算法中的用户冷启动问题,并在平均绝对误差和均方根误差2个评价指标上优于已有算法.

关键词: 推荐系统, 协同过滤, 冷启动, 社区, 专家信息

Abstract: Collaborative filtering recommendation algorithm has been widely used because it is not limited by the knowledge in a specific domain and easy to implement. However, it is faced with the problem of several issues such as data sparsity, extensibility and cold start which affect the effectiveness of the recommendation algorithm in some practical application scenarios. To address the user cold start problem, by merging social trust information (i.e., trusted neighbors explicitly specified by users) and rating information, a collaborative filtering recommendation algorithm based on information of community experts is proposed in this paper. First of all, users are divided into different communities based on their social relations. Then, experts in each community are identified according to some criteria. In addition, in order to alleviate the impact of the data sparsity, ratings of an expert’s trusted neighbors are merged to complement the ratings of the expert. Finally, the prediction for a given item is generated by aggregating the ratings of experts in the community of the target user. Experimental results based on two real-world data sets FilmTrust and Epinions show the proposed algorithm is able to alleviate the user cold start problem and superior to other algorithms in terms of MAE and RMSE.

Key words: recommendation system, collaborative filtering, cold start, community, expert information

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