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

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

ISBN-10: 3319066048

ISBN-13: 9783319066042

ISBN-10: 3319066056

ISBN-13: 9783319066059

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2014, held in Tainan, Taiwan, in may possibly 2014. The forty complete papers and the 60 brief papers awarded inside those court cases have been rigorously reviewed and chosen from 371 submissions. They hide the overall fields of development mining; social community and social media; type; graph and community mining; purposes; privateness maintaining; advice; characteristic choice and relief; computing device studying; temporal and spatial facts; novel algorithms; clustering; biomedical facts mining; circulate mining; outlier and anomaly detection; multi-sources mining; and unstructured information and textual content mining.

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Extra resources 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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Labels, were not considered. In addition, offline models are not suitable in online scenario with large continuous incoming data. In online RS, Diaz-Aviles et al. [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.

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.

Knowl. Discov. 22(1-2), 31–72 (2011) 13. : Trust based recommender system for semantic web. In: IJCAI, pp. tw Abstract. This paper presents a novel framework for dynamic circle recommendation for a query user at a given time point from historical communication logs. We identify the fundamental factors that govern interactions and aim to automatically form dynamic circle for scenarios, such as, who should I dial to in the early morning? whose mail would I reply first at midnight? We develop a time-sensitive probabilistic model (TCircleRank) that not only captures temporal tendencies between the query user and candidate friends but also blends frequency and recency into group formation.

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Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao


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