Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (8): 2246-2251.DOI: 10.11772/j.issn.1001-9081.2016.08.2246

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Collaborative filtering recommendation method based on improved heuristic similarity model

ZHANG Nan, LIN Xiaoyong, SHI Shenghui   

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • Received:2016-01-13 Revised:2016-03-03 Online:2016-08-10 Published:2016-08-10
  • Supported by:
    This work is partially supported by the Fundamental Research Funds for the Central Universities (JD1413).


张南, 林晓勇, 史晟辉   

  1. 北京化工大学 信息科学与技术学院, 北京 100029
  • 通讯作者: 林晓勇
  • 作者简介:张南(1988-),男,湖南邵阳人,硕士研究生,主要研究方向:人工智能、数据挖掘;林晓勇(1979-),男,福建浦城人,副教授,博士研究生,主要研究方向:基于Web2.0的社会性网络服务、数据挖掘;史晟辉(1974-),女,河北河间人,副教授,博士研究生,主要研究方向:大数据分析、编译技术、生物信息、自然语言处理。
  • 基金资助:

Abstract: In order to improve the accuracy and efficiency of collaborative filtering recommendation method, a collaborative filtering recommendation method based on improved heuristic similarity model, namely PSJ, was proposed, which considered the difference of user ratings, the user global rating preferences and the number of common rating items. The Proximity factor of PSJ method used the exponential function to reflect the influence of the difference of user ratings, which avoided the problem of zero divider. The Significance factor of NHSM (New Heuristic Similarity Model) method and the URP (User Rating Preference) factor were merged to build the Significance factor of PSJ method, which makes the computational complexity of the PSJ method be lower than that of NHSM. To improve the recommendation performance in data sparsity conditions, both the variance value of user ratings and user global rating preferences were considered in PSJ method. In experiments, precision and recall of Top-k recommendation were used to evaluate the results. The results show that compard with NHSM, Jaccard algorithm, Adjust COSine similarity (ACOS) algorithm, Jaccard Mean Squared Difference (JMSD) algorithm and Sigmoid function based Pearson Correlation Coefficient method (SPCC), the precision and recall of PSJ method are improved.

Key words: collaborative filtering recommendation method, heuristic similarity model, user similarity, recommendation performance, data sparsity

摘要: 为提高协同过滤推荐方法的准确性和有效性,提出一种基于改进型启发式相似度模型的协同过滤推荐方法PSJ。该方法考虑了用户评分差值、用户全局评分偏好和用户共同评分物品数三个因素。PSJ方法的Proximity因子使用指数函数反映用户评分差值对用户相似度的影响,这样也可避免零除问题;将NHSM方法中的Significance因子和URP因子合并成PSJ方法的Significance因子,这使得PSJ方法的计算复杂度低于NHSM方法;而且为了提高在数据稀疏情况下的推荐效果,PSJ方法同时考虑了用户间的评分差值和用户全局评分两个因素。实验采用Top-k推荐中的查准率和查全率作为衡量标准。实验结果表明,当推荐物品数大于20时,与NHSM、杰卡尔德算法、自适应余弦相似度(ACOS)算法、杰卡尔德均方差(JMSD)算法和皮尔逊相关系数算法(SPCC)相比,PSJ方法的查准率与查全率均有提升。

关键词: 协同过滤推荐方法, 启发式相似度模型, 用户相似度, 推荐效果, 数据稀疏

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