Abstract
Current social media services like Twitter and Sina Weibo have become an indispensable platform, and provide a large number of real-time messages. However, users are often overwhelmed with large amounts of information delivered via their followees, and may miss out on much enjoyable or useful content. An information overload problem has troubled many users, especially those with many followees and thousands of tweets arriving every day. In this case, real-time personalized recommendation plays an extreme important role in microblog, which needs analyzing users’ preference and recommending most relevant and newest content. Both of them pose serious challenges. In this paper, we focus on personal online tweet recommendation and propose a Collaborative Tweet Ranking Online Framework (CTROF) for the recommendation, which has integrated the Optimized Collaborative Tweet Ranking model CTR+ and Reservoir Sampling algorithm together. The experiment conducted on a real dataset from Sina microblog shows good performance and our algorithm outperforms the other baseline methods.
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Song, K., Wang, D., Feng, S., Zhang, Y., Qu, W., Yu, G. (2014). CTROF: A Collaborative Tweet Ranking Framework for Online Personalized Recommendation. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_1
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DOI: https://doi.org/10.1007/978-3-319-06605-9_1
Publisher Name: Springer, Cham
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