IEEE transactions on components, packaging, and by Institute of Electrical and Electronics Engineers.;

By Institute of Electrical and Electronics Engineers.; Components, Packaging & Manufacturing Technology Society.; IEEE TAB Steering Committee on Design and Manufacturing Engineering

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Read or Download IEEE transactions on components, packaging, and manufacturing technology. : Part C, Manufacturing a publication of the IEEE Components, Packaging, and Manufacturing Technology Society ; IEEE TAB Steering Committee on Design and Manufacturing Engineering PDF

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Extra resources for IEEE transactions on components, packaging, and manufacturing technology. : Part C, Manufacturing a publication of the IEEE Components, Packaging, and Manufacturing Technology Society ; IEEE TAB Steering Committee on Design and Manufacturing Engineering

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770-777 [2] H. V. Dyke, and A. Vivacqua, In: Proceedings of the International Conference on Intelligent User Interfaces. Los Angeles, CA: ACM Press, (1999), p. 65-68 [3] J. Konstan, B. Miller, D. Maltz, et al: Communications of the ACM, Vol. 40 (1997), p. 77-87 [4] U. Shardanand and P. Maes: Proceedings of the ACM CHI’95 Conference on Human Factors in Computing Systems. New York: ACM Press, (1995), p. 210-217 [5] D. Goldberg, D. M. Oki and D. Terry: Communications of the ACM, Vol. 35 (1992). H.

And the deflection of iteration step i+1 is calculated.

Borouchaki, P. L. 233-259. 29, pp. 1823-1833 [11] H. L. Lu: Advanced Material Research, Vols. 69-70 (2009), pp. cn Keywords: Collaborative filtering, Similarity computing, Fashion recommendation. Abstract. Collaborative filtering (CF) technique is the most successful method for recommendation system. In this article, we developed a fashion recommendation system by using CF technique. In order to improve on data sparseness problems in CF technique, firstly we built users’ similarities based on users’ background information which is related with fashion, then the neighbors’ predicting ratings were filled into the U-I rating matrix in advance before the traditional collaborative filtering.

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