Download Advances in Knowledge Discovery and Data Mining: 18th by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, PDF

By Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2014, held in Tainan, Taiwan, in could 2014. The forty complete papers and the 60 brief papers awarded inside of those court cases have been conscientiously reviewed and chosen from 371 submissions. They disguise the overall fields of trend mining; social community and social media; category; graph and community mining; purposes; privateness conserving; advice; characteristic choice and aid; desktop studying; temporal and spatial information; novel algorithms; clustering; biomedical information mining; movement mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.

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Extra info for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II

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The rest of this paper is organized as follows. In Section 2, we introduce the related work. Our recommendation framework is formulated in Section 3, and experimental results are reported in Section 4. Section 5 is the conclusion. 1 Related Work Matrix Factorization(MF) Matrix factorization is one of the most popular approaches for low-dimensional matrix decomposition. Here, we review the basic MF method [4]. The rating matrix R∈RM×N (M is the number of users and N is the number of items) can be predicted by U V T with the user latent factor matrix U ∈RD×M and item latent factor matrix V ∈RD×N , where D is the dimension of the vectors.

13] proposed a RMFX online framework, however it had complex sampling algorithm and just considered hashtags of tweet. Work for ranking tweets also includes [14,15]. In this paper, we focus on recommending tweets in real time by integrating offline model and stream sampling algorithm together. We propose a novel CTROF framework by improving the drawbacks of Chen and Diaz-Aviles’s work and absorbing their advantages. For this purpose, we first review CTR model briefly and propose an innovative CTR+ model by considering content, social relation and hashtags in Section 3.

User Profile Training Set Retweet Post NonRetweet Pose users History Data Initial Modeling CTR+ Onling Updating CTR+ Fig. 1. Overview of CTROF in sampling and modeling online tweet stream To the best of our knowledge, this work is the first experimental study to integrate tweet content (past and online), social structure and personal profile into collaborative filter model, and demonstrate a practical online personalized tweet recommendation framework. To summarize, the main contributions of our work are as follows.

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