Download Computational Collective Intelligence. Technologies and by Marcin Maleszka, Ngoc Thanh Nguyen (auth.), Piotr PDF

By Marcin Maleszka, Ngoc Thanh Nguyen (auth.), Piotr Jędrzejowicz, Ngoc Thanh Nguyen, Kiem Hoang (eds.)

The two-volume set LNAI 6922 and LNAI 6923 constitutes the refereed lawsuits of the 3rd foreign convention on Computational Collective Intelligence, ICCCI 2011, held in Gdynia, Poland, in September 2011.
The 112 papers during this quantity set awarded including three keynote speeches have been conscientiously reviewed and chosen from three hundred submissions. The papers are geared up in topical sections on wisdom administration, computing device studying and functions, self reliant and collective decision-making, collective computations and optimization, net prone and semantic net, social networks and computational swarm intelligence and applications.

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Additional info for Computational Collective Intelligence. Technologies and Applications: Third International Conference, ICCCI 2011, Gdynia, Poland, September 21-23, 2011, Proceedings, Part II

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For example, in the case of a system proposed in [2], the content-based recommender builds a model for each user and then the user rating data is combined with the product features. 2 Objectives The main aim of the research presented in this paper is to evaluate a new hybrid recommendation method that is based on low-dimensional feature augmentation and which uses two sources of information – content feature data and collaborative filtering data. We analyze the results of using Singular Value Decomposition (SVD) as a dimensionality reduction method for first-stage content feature modeling.

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Information Systems 22(1), 5–53 (2004) 10. : Matrix factorization techniques for recommender systems. IEEE Computer 42(8), 30–37 (2009) 11. : Semantically enhanced collaborative filtering on the web. , Stumme, G. ) EWMF 2003. LNCS (LNAI), vol. 3209, pp. 57–76. Springer, Heidelberg (2004) 12. : Preserving recommender accuracy and diversity in sparse datasets. The International Journal on AI Tools 13(1), 219–235 (2004) 13. : Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences.

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