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Recommending Microblogs Based on Emotion-Weighted Association Rules |
Li Tiejun,Yan Duanwu( ),Yang Xiongfei |
School of Economics & Management, Nanjing University of Science and Technology, Nanjing 210094, China |
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Abstract [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.
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Received: 26 June 2019
Published: 01 June 2020
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Corresponding Authors:
Yan Duanwu
E-mail: yanwu123@sina.com
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