[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.
李铁军,颜端武,杨雄飞. 基于情感加权关联规则的微博推荐研究*[J]. 数据分析与知识发现, 2020, 4(4): 27-33.
Li Tiejun,Yan Duanwu,Yang Xiongfei. Recommending Microblogs Based on Emotion-Weighted Association Rules. Data Analysis and Knowledge Discovery, 2020, 4(4): 27-33.
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