Abstract
Sina Weibo allows users to create tags enclosed in a pair of # which are called microtopics. Each microtopic has a designate page, and can be directly visited and commented on. Microtopic recommendation can facilitate users to efficiently acquire information by summarizing trending online topics and feeding comments with high quality. However, it is non-trivial to recommend microtopics to the users of Sina Weibo to satisfy their information needs. In this paper, we focus on personalized microtopic recommendation. Collaborative filtering based methods only utilize the user adoption matrix, while content based methods only use textual information. However, both of them can not achieve satisfactory performance in real scenarios. Moreover, auxiliary information on social media provides great potential to improve the recommendation performance. Therefore, we propose a novel hierarchical Bayesian model integrating user adoption behaviors, user item content information, and rich contextual information into the same principled model. We experiment with different kinds of textual and contextual information from both user and microtopic sides on a real dataset. Experimental results show that our model significantly outperforms a few baseline methods.
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Acknowledgements
We thank the anonymous reviewers for their constructive comments, and gratefully acknowledge the support of the National Basic Research Program (973 Program) of China via Grant 2014CB340503, the National Natural Science Foundation of China (NSFC) via Grant 61133012 and 61472107. We thank Minghui Qiu for helping to improve the work.
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Li, Y., Jiang, J., Liu, T., Sun, X. (2015). Personalized Microtopic Recommendation with Rich Information. In: Zhang, X., Sun, M., Wang, Z., Huang, X. (eds) Social Media Processing. SMP 2015. Communications in Computer and Information Science, vol 568. Springer, Singapore. https://doi.org/10.1007/978-981-10-0080-5_1
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DOI: https://doi.org/10.1007/978-981-10-0080-5_1
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