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数据分析与知识发现  2019, Vol. 3 Issue (8): 62-67     https://doi.org/10.11925/infotech.2096-3467.2018.1000
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于物品质量和用户评分修正的协同过滤推荐算法 *
焦富森,李树青()
南京财经大学信息工程学院 南京 210046
Collaborative Filtering Recommendation Based on Item Quality and User Ratings
Fusen Jiao,Shuqing Li()
College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210046, China
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摘要 

【目的】在个性化推荐中, 考虑物品质量和用户评分倾向性对用户打分的影响, 提高推荐效果。【方法】提出一种改进的协同过滤推荐算法: 利用物品质量评估算法实现了用户评分修正, 可以改进查找与用户兴趣相似的最近邻过程, 并在此基础上进行推荐。【结果】利用MovieLens数据集进行测试, 与传统协同过滤算法相比, 改进算法的MAE提高4.7%; 与其他几种改进算法相比, 精确度均有不同程度的提高。【局限】只关注现有的评分修正, 并没有考虑用户的兴趣漂移, 在一定程度上影响推荐效果。【结论】本文提出的算法推荐结果更加精确, 有效地减少了物品质量和用户评分倾向性对推荐结果的影响, 提高了推荐质量。

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焦富森
李树青
关键词 推荐系统协同过滤物品质量评分修正    
Abstract

[Objective] This paper proposes a modified collaborative filtering algorithm, aiming to improve the results of personalized recommendations. [Methods] First, we evaluated item quality and corrected user ratings based on their previous records. Then, we identified users with similar interests to generate better recommendations. [Results] We tested the new algorithm on MovieLens dataset and found the MAE was 4.7% higher than those of the traditional or other modified methods. [Limitations] The new algorithm does not address the interests drifting issues. [Conclusions] The proposed algorithm could recommend products to consumers more effectively.

Key wordsRecommender System    Collaborative Filtering    Item Quality    User Rating Correction
收稿日期: 2018-08-25      出版日期: 2019-09-29
ZTFLH:  TP391 G35  
基金资助:*本文系江苏省研究生科研与实践创新计划项目“面向个性化推荐服务的互联网用户画像关键技术研究”(KYCX17_1208);国家社会科学基金项目“基于大数据分析的数字图书馆个性化服务模式创新研究”的研究成果之一(16BTQ030)
通讯作者: 李树青     E-mail: leeshuqing@163.com
引用本文:   
焦富森,李树青. 基于物品质量和用户评分修正的协同过滤推荐算法 *[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings. Data Analysis and Knowledge Discovery, 2019, 3(8): 62-67.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1000      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I8/62
  用户电影二部图
Average HITS Bayes IMDB
Planet Earth Forrest Gump The Shawshank Redemption The Shawshank Redemption
The Shawshank Redemption Pulp Fiction The Godfather The Godfather
The Usual Suspects The Matrix The Usual Suspects The Godfather: Part II
Schindler’s List The Shawshank Redemption The Godfather: Part II The Dark Knight
The Godfather: Part II The Silence of the Lambs Schindler’s List 12 Angry Men
12 Angry Men Jurassic Park Seven Samurai Schindler’s List
Fight Club Star Wars: Episode IV Fight Club The Lord of the Rings: The Return of the King
Pulp Fiction Star Wars: Episode V 12 Angry Men Pulp Fiction
Planet Earth Terminator 2: Judgment Day Spirited Away Il buono, il brutto, il cattivo
Human Planet Braveheart Pulp Fiction Fight Club
  Item质量实验对比
  Item质量实验对比
  算法对比实验
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