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数据分析与知识发现  2018, Vol. 2 Issue (5): 94-104     https://doi.org/10.11925/infotech.2096-3467.2017.1009
  应用论文 本期目录 | 过刊浏览 | 高级检索 |
面向位置的多样性兴趣新闻推荐研究*
花凌锋1, 杨高明1(), 王修君2
1安徽理工大学计算机科学与工程学院 淮南 232001
2安徽工业大学计算机科学与技术学院 马鞍山 243032
Recommending Diversified News Based on User’s Locations
Hua Lingfeng1, Yang Gaoming1(), Wang Xiujun2
1School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
2School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China
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摘要 

【目的】 针对基于位置的混合推荐方法存在的相似度算法准确率低下和系统已有用户新位置冷启动问题, 提出面向位置的多样性兴趣新闻推荐方法(DLR)。【方法】 使用聚类算法对用户历史行为数据的位置标签进行聚类分析, 再利用LDA模型和基于三维用户相似度算法的协同过滤技术为每个聚类位置分别建立一个偏好模型。【结果】 推荐时通过GPS获取当前位置信息并确定相应的偏好模型, 在此基础上生成两个偏好列表, 分别截取偏好列表的Top-n, 组成推荐新闻集。当用户处于新位置时, 使用基于降维相似度算法的协同过滤技术生成推荐列表并截取Top-n, 生成多样性推荐新闻集。【局限】未能解决系统新用户的冷启动问题。【结论】 DLR方法在新闻推荐的多样性和准确性上均有明显提升, 提高了用户的阅读满意度。

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花凌锋
杨高明
王修君
关键词 新闻推荐用户相似度位置服务协同过滤    
Abstract

[Objective] Location-based hybrid recommendation methods are not accurate and have cold-start problem of the existing users in new locations, because they do not incorporate the location information of users well into their design. This paper proposes the Diversity news Location-oriented Recommendation algorithm (DLR), aiming to improve the performance of traditional methods. [Methods] First, we clustered the location tags from users’ historical behavior data. Then, we used the LDA model and the classic collaborative filtering algorithm based on 3D similarity to establish a preference model for each position cluster. Finally, we obtained a user’s current position with the help of GPS, and selected a preference cluster model for this user. [Results] The proposed method generated two preference lists, and chose the Top-n of the two lists as recommended news for the user. [Limitations] The proposed method could not effectively solve the cold start issue facing new users. [Conclusions] The DLR model could improve the diversity and accuracy of recommended news.

Key wordsNews Recommendation    User Similarity    Location Based Service    Collaborative Filtering
收稿日期: 2017-10-09      出版日期: 2018-06-20
ZTFLH:  TP181  
基金资助:*本文系国家自然科学基金项目“差分隐私高维数据发布理论与方法研究”(项目编号: 61572034)、国家自然科学基金项目“滑动窗口上数据流副本近似检测算法及其空间复杂度下界研究”(项目编号: 61402008)和安徽省高校自然科学基金项目“基于谱聚类的流式大数据隐私保护研究”(项目编号: KJ2014A061)的研究成果之一
引用本文:   
花凌锋, 杨高明, 王修君. 面向位置的多样性兴趣新闻推荐研究*[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
Hua Lingfeng,Yang Gaoming,Wang Xiujun. Recommending Diversified News Based on User’s Locations. Data Analysis and Knowledge Discovery, 2018, 2(5): 94-104.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1009      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I5/94
  移动新闻推荐模型流程
软\硬件 配置
操作系统 Win 7旗舰版
Hardware CPU 3.4GHz、4GB、1TB
Software Python 3.5 64bit
  实验环境及配置
  不同位置下用户偏好新闻主题分布
  历史位置下的F-measure
  新位置下的F-measure
  历史位置下各推荐方法的Diversity
  新位置下各推荐方法的Diversity
  历史位置各推荐方法的F-measure
  新位置各推荐方法的F-measure
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