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数据分析与知识发现  2016, Vol. 32 Issue (12): 44-49    DOI: 10.11925/infotech.1003-3513.2016.12.06
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
一种基于相对相似性提高推荐总体多样性的协同过滤算法
姜书浩1,2(),张立毅1,2,张志鑫2
1天津大学电子信息工程学院 天津 300072
2天津商业大学信息工程学院 天津 300134
New Collaborative Filtering Algorithm Based on Relative Similarity
Shuhao Jiang1,2(),Liyi Zhang1,2,Zhixin Zhang2
1School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
2Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China
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摘要 

目的】以提高推荐系统的总体多样性为出发点, 解决因为用户评分数据分布不均和稀疏造成的误差从而影响推荐精确性和多样性问题。【方法】根据用户间共同评分项目的数量, 通过加权计算得出相对相似性指数, 修正相似性计算方法, 进而优化预测评分算法, 在保证推荐精确性的前提下提高总体多样性, 提升企业的长尾营销效果。【结果】实验结果表明, 当评分阈值为3.5, 最近邻数目为20时, 本文方法在MovieLens数据集上的计算结果相对于采用传统的余弦相似性计算结果, 总体多样性提高了114, 精确性提高了6.5%。【局限】仅适用于基于最近邻的协同过滤算法, 并不涉及其他推荐技术。【结论】该方法有效地提高了推荐的总体多样性, 获得推荐精确性和总体多样性用户相对满意度都较高的推荐结果。

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姜书浩
张立毅
张志鑫
关键词 总体多样性相对相似性协同过滤    
Abstract

[Objective]The purpose of this study is to improve the overall diversity of the recommendation results. The proposed algorithm reduces errors caused by the uneven distribution and sparsity of user rating data, and then improves the recommendation accuracy and diversity. [Methods] We first generated the relative similarity index based on the number of common ratings and individual weights. Second, we modified the similarity calculation method, and the rating prediction algorithm. The proposed model improved the aggregated diversity and maintained the recommendation accuracy, which improved the marketing effects. [Results] The aggregated diversity index increased 114, the accuracy improved 6.5% on the MovieLens data compared with results generated by the traditional cosine similarity calculation, (the rating threshold was 3.5 and number of KNN is 20). [Limitations] This method was only applicable to collaborative filtering based on the nearest neighbor, and it did not include other recommendation techniques. [Conclusions] The proposed method effectively improves the diversity and accuracy of recommendation results, which significantly improves the user experience.

Key wordsAggregate diversity    Relative similarity    Collaborative filtering
收稿日期: 2016-08-15     
引用本文:   
姜书浩, 张立毅, 张志鑫. 一种基于相对相似性提高推荐总体多样性的协同过滤算法[J]. 数据分析与知识发现, 2016, 32(12): 44-49.
Shuhao Jiang, Liyi Zhang, Zhixin Zhang. New Collaborative Filtering Algorithm Based on Relative Similarity. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.1003-3513.2016.12.06.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2016.12.06
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