Download e-book for iPad: Advances in Knowledge Discovery and Data Mining: 17th by Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian

By Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)

ISBN-10: 3642374557

ISBN-13: 9783642374555

ISBN-10: 3642374565

ISBN-13: 9783642374562

The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed court cases of the seventeenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the full of ninety eight papers provided in those court cases was once conscientiously reviewed and chosen from 363 submissions. They conceal the final fields of knowledge mining and KDD largely, together with development mining, class, graph mining, functions, desktop studying, characteristic choice and dimensionality relief, a number of details assets mining, social networks, clustering, textual content mining, textual content category, imbalanced info, privacy-preserving facts mining, suggestion, multimedia facts mining, movement information mining, information preprocessing and representation.

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Extra resources for Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II

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Cai et al. Table 1. 379 Results. A comparison of ProCF and Best2CF+ on the test set in terms of the evaluation metrics is shown in Fig. 3. Clearly ProCF outperforms Best 2CF+ on precision for all Top-N recommendations; the most significant comparative improvement is on Top 100 where ProCF outperforms Best 2CF+ by 34%. SRI shows that although Best 2CF+ improves the baseline performance of the system for all Top-N , ProCF achieves greater improvement. Since [12] show that Best 2CF+ outperforms traditional CFs on P2P recommendation, ProCF has a clear advantage.

203–210 (2009) 9. : Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011) 10. : Trust-aware recommender systems. In: RecSys, pp. 17–24 (2007) 11. : Recommending collaboration with social networks: a comparative evaluation. In: CHI, pp. 593–600 (2003) 12. : Probabilistic matrix factorization. , Switzerland Abstract. We study the problem of identifying representative users in social networks from an information spreading perspective. While traditional network measures such as node degree and PageRank have been shown to work well for selecting seed users, the resulting nodes often have high neighbour overlap and thus are not optimal in terms of maximising spreading coverage.

In: Proc. HCI 1995, pp. 210–217 (1995) 19. : Social matching: A framework and research agenda. ACM Transactions on Computer-Human Interaction 12(3), 401–434 (2005) 20. : Probabilistic memorybased collaborative filtering. cn Abstract. Social network based applications such as Facebook, Myspace and LinkedIn have become very popular among Internet users, and a major research problem is how to use the social network information to better infer users’ preferences and make better recommender systems. A common trend is combining the user-item rating matrix and users’ social network for recommendations.

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Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II by Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)


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