By Zbigniew Michalewicz (auth.), Harris Papadopoulos, Andreas S. Andreou, Max Bramer (eds.)
The abundance of knowledge and elevate in computing energy at the moment let researchers to take on hugely advanced and demanding computational difficulties. ideas to such difficulties are actually possible utilizing advances and recommendations from the world of synthetic Intelligence. the overall concentration of the AIAI convention is to supply insights on how man made Intelligence will be utilized in real-world occasions and serve the research, research and modeling of theoretical and functional matters. This quantity includes papers chosen for presentation on the sixth IFIP convention on synthetic Intelligence functions and suggestions (AIAI 2010) and held in Larnaca, Cyprus, in the course of October 6–7, 2010. IFIP AIAI 2010 used to be co-organized by way of the college of Cyprus and the Cyprus collage of expertise and used to be subsidized via the Cyprus collage of know-how, Frederick college and the Cyprus Tourism association. AIAI 2010 is the reliable convention of the WG12.5 “Artificial Intel- gence purposes” operating team of IFIP TC12, the overseas Federation for info Processing Technical Committee on man made Intelligence (AI). AIAI is a convention that grows in value each year attracting researchers from assorted nations all over the world. It continues prime quality, criteria and welcomes study papers describing technical advances and engineering and ind- trial functions of clever platforms. AIAI 2010 used to be now not restricted to introducing how AI can be utilized in real-life events, but additionally incorporated cutting edge tools, options, instruments and ideas of AI expressed on the algorithmic or systemic level.
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Additional resources for Artificial Intelligence Applications and Innovations: 6th IFIP WG 12.5 International Conference, AIAI 2010, Larnaca, Cyprus, October 6-7, 2010. Proceedings
However, the threshold value does not actually aﬀect the experiments, recommendation eﬃciency will remain. Removing the thresholds will lead to negative recommendations which is out of scope for this paper. Let cji be the i-th member of the j-th cluster and cjr be the representative of that cluster. Let us, also, denote the set of items rated by cji as Iji and the set of high rated items of the same user as Hji (obviously Hji ⊆ Iji ). For each cluster member two values are computed: the satisfaction without recommendation sji and the satisfaction after receiving recommendation by the cluster representative sji .
In: Information and Communications Security. LNCS, vol. 5927, pp. 411–424. Springer, Heidelberg (2009) 23. : Improving Recommendation Lists Through Topic Diversiﬁcation. In: 14th International World Wide Web Conference (WWW 2005), Chiba, Japan, pp. 22–32 (2005) An Optimal Scaling Approach to Collaborative Filtering Using Categorical Principal Component Analysis and Neighborhood Formation Angelos I. Markos1, Manolis G. Vozalis2 , and Konstantinos G. gr Abstract. Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations.
A neural-network approach to statistical pattern classification by semiparametric estimation of a probability density functions. IEEE Trans. Neural Networks 2, 366–377 (1991) 12. : Transvaginal ultrasound measurement ofendometrial thickness as a biomarker for estrogen exposure. Cancer Epidemiology Biomarkers 13(9), 1459–1465 (1968) 13. : A classification technique based on radial basis function neural networks. Advances in Engineering Soft2ware 37(4), 218–221 (2006) 14. : A software system for the didactic utilization of classroom observation for Informatic courses.