[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.
( Sun Guangming, Wang Shuo, Zou Jingzhao . Collaborative Filtering Recommendation Algorithm Measured by Compound Multiple Factors[J]. Application Research of Computers, 2015,32(10):2896-2900.)
Chen L, Chen G, Wang F . Recommender Systems Based on User Reviews: The State of the Art[J]. User Modeling and User-Adapted Interaction, 2015,25(2):99-154.
Qiu L, Gao S, Cheng W , et al. Aspect-based Latent Factor Model by Integrating Ratings and Reviews for Recommender System[J]. Knowledge-Based Systems, 2016,110:233-243.
( Liu Huiting, Cheng Lei, Guo Xiaoxue , et al. Real-time Personalized Micro-Blog Recommendation System[J]. Computer Science, 2018,45(9):253-259, 265.)
Lemire D, Maclachlan A. Slope One Predictors for Online Rating-Based Collaborative Filtering[C]// Proceedings of the 2005 SIAM Data Mining Conference (SDM’05), Newport Beach, California, USA. 2005: 21-23.
( Cai Shuqin, Yuan Qian, Zhou Peng , et al. Collaborative Filtering Recommendation Model in Micro-Blogging Website Based on Information Diffusion Theory[J]. System Engineering-Theory & Practice, 2015,35(5):1267-1275.)
( Tang Huifeng, Tan Songbo, Cheng Xueqi . Research on Sentiment Classification of Chinese Reviews Based on Supervised Machine Learning Techniques[J]. Journal of Chinese Information Processing, 2007,21(6):88-94.)
Dasgupta S, Ng V. Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification[C]// Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009: 701-709.
( Yu Yisheng, Xu Jianbin, Liu Xinyan . Research on Personalized Information Recommendation Based on Community Structure Mining[J]. Journal of the China Society for Scientific and Technical Information, 2017,36(10):1093-1098.)