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 lawsuits 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 awarded in those complaints used to be rigorously reviewed and chosen from 363 submissions. They conceal the overall fields of knowledge mining and KDD widely, together with development mining, class, graph mining, purposes, computer studying, function choice and dimensionality relief, a number of details resources mining, social networks, clustering, textual content mining, textual content class, imbalanced info, privacy-preserving info mining, advice, multimedia facts mining, movement info mining, facts preprocessing and representation.

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LNCS, vol. 6464, pp. 476–485. Springer, Heidelberg (2010) 5. : Learning collaborative filtering and its application to people to people recommendation in social networks. In: Proc. ICDM 2010, pp. 743–748 (2010) 6. : Novelty and diversity metrics for recommender systems: Choice, discovery and relevance. In: DDR 2011 Workshop at ECIR 2011 (2011) 7. : Memory-based weighted-majority prediction for recommender systems. In: ACM SIGIR 1999 Workshop Recommender Systems (1999) 8. : Item-based Top-N Recommendation Algorithms.

There are several things worth mentioning. First, it shows social network information is valuable. Social network based approaches outperformed baseline SVD. Second, it shows A-Neighborhood performs better than other methods. The improvement of using neighbors over SVD is not surprising. Because factorization captures global structure of the rating matrix, while neighborhood captures local regularization of the data space. Combining these complementary information has the same effect as the Netflix competition winner’s solution, which combines nearest neighbors with factorization models [7].

Test) sets contained 166699 (95814) users with a total of 1710332 (436128) interaction tuples. Of these 264142 (66482) were positive interactions and 1446190 (369646) were negative (including nonreplies). 2%). Methodologies. We compare the proposed algorithm with the P2P Best 2CF+ method [12] on their evaluation metric and a new metric defined below. As far as we are aware this is the best-performing published CF method for recommendation in online dating. Model-based methods such as matrix factorisation methods were also tested but were not able to handle such a large dataset.

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