%A Li Tiejun,Yan Duanwu,Yang Xiongfei %T Recommending Microblogs Based on Emotion-Weighted Association Rules %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.0765 %P 27-33 %V 4 %N 4 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4812.shtml} %8 2020-04-25 %X

[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.