基于情感加权关联规则的微博推荐研究  

Recommending Microblogs Based on Emotion-Weighted Association Rules

作　　者：李铁军 颜端武[1] 杨雄飞 Li Tiejun;Yan Duanwu;Yang Xiongfei(School of Economics&Management,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学经济管理学院,南京210094

出　　处：《数据分析与知识发现》2020年第4期27-33,共7页Data Analysis and Knowledge Discovery

基　　金：江苏省普通高校专业学位研究生创新计划项目“网络微博主题情感挖掘及个性化推荐应用研究”(项目编号:SJCX18_0134);装备发展部技术基础项目“微博信息采集与个性化推送研究”(项目编号:YXNLG20171QB02);江苏省社会科学基金项目“领域知识分析视角下文献知识关联揭示及应用研究”(项目编号:17TQB009)的研究成果之一。

摘　　要：【目的】结合用户访问历史数据,向用户推荐其感兴趣的微博,进一步提高用户体验和服务效果。【方法】对用户的微博评论历史行为进行关联规则挖掘,得到所有被评论微博的频繁1-项集;运用情感词典计算微博评论文本的情感强度,将情感强度超过阈值的微博生成新的频繁1-项集,继续进行关联规则挖掘,将强关联规则用于微博推荐。【结果】相较于单纯关联规则推荐、基于内容相似推荐的基准推荐算法,本文方法在准确率、覆盖率和F值评价指标上均有10%左右提升。【局限】实验中对于参数的取值较为粗略,可能导致无法得到最优参数。【结论】基于情感加权关联规则的微博推荐方法可以更好地进行微博推荐。[Objective] This study recommends microblogs based on readers’ browsing behaviors, aiming to improve users’ experience with the Weibo services. [Methods] Firstly, we used association rules to analyze users’ behaviors on Sina Weibo and retrieved all frequent 1-item sets for comments. Then, we calculated the emotional intensity of comments, and identified micro-blog posts with emotional intensity higher than the threshold. Finally,we generated a new frequent 1-item set to establish stronger association rules for the final list. [Results]Compared with the benchmark recommendation algorithms, the accuracy, recall and F values of the proposed algorithm were all improved by 10%. [Limitations] The parameters in our experiment were relatively simple,which might not yield the best results. [Conclusions] The proposed method based on emotion-weighted association rules can effectively recommend microblogs.

关 键 词：关联规则 情感分析 情感加权 微博推荐 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]