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

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed complaints of the 18th Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2014, held in Tainan, Taiwan, in may well 2014. The forty complete papers and the 60 brief papers provided inside those complaints have been rigorously reviewed and chosen from 371 submissions. They conceal the final fields of trend mining; social community and social media; type; graph and community mining; functions; privateness maintaining; advice; function choice and aid; computing device studying; temporal and spatial info; novel algorithms; clustering; biomedical info mining; move mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.

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Read Online or Download Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II PDF

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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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Contextaware algorithms [6] that incorporate contextual information have improved the accuracy. With the popularity of online social networks, social recommendation models [7,8,9,10,11] that incorporate social networks information (Figure 1(b)) not only improve the recommendation quality, but also solve the cold start problem. Even so, there are still some drawbacks. They typically model users’ rating for every item. As the number of items increases, the rating matrix becomes very large so that matrix operations in all CF algorithms become exceedingly expensive which may even go beyond the physical computation/storage power.

All algorithms are implemented using this library. 3 Impacts of Different Factors The Number of Layers of Category: The difference between LLR1 and LLR2 is the number of layers of category. 2739. Contrasts looked, LLR2 has smaller prediction error than LLR1 in phase one. So our framework benefits more from the top-down approaches than the flat approach. We believe classification based on hierarchy can better model the similarity among items. gov/javanumerics/jama/ 22 K. Ji et al. (a) MAE for LLR1 (b) MAE for LLR2 (c) RMSE for LLR1 (d) RMSE for LLR2 Fig.

12 K. Song et al. Future work includes further analyzing semantics of content and studying more accurate and efficient sampling methods for improving the recommendation quality. We will consider more media factors in tweet such as images and videos. In addition, the tensor factorization for tweet recommendation is also our future direction. Acknowledgements. This work is supported by the State Key Development Program for Basic Research of China (Grant No. 2011CB302200-G), State Key Program of National Natural Science of China (Grant No.

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