Read e-book online Advances in Knowledge Discovery and Data Mining: 17th PDF

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

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

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Extra info 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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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. However, existing solutions add the social network information for a particular user without considering the different characteristics of the products to be recommended and the neighbors involved.

Previous works on this task tend to adopt classic IR measures such as P@N and Recall [4][2]. However, without complete relevance judgements for each individual user, standard IR evaluation is almost infeasible. Thus we use a variation of the evaluation method in [7]. We randomly sample 10% from the rating data set i, j, ri,j . Then for each user in the sampled data set, we randomly choose one user-item pair with a 5-star rating. This gives 15,025 user-item testing pairs. To simulate the scenario that we only want to recommend the 5-star items to users, we treat 5-star pairs as relevant.

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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