Applying Collaborative Filtering Techniques for Individual Fashion Recommendation

Jian Lan Lei; Jin Wang; Guo Dong Lu; Shao Mei Fei

doi:10.4028/www.scientific.net/AMR.102-104.31

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 102-104Applying Collaborative Filtering Techniques for...

Applying Collaborative Filtering Techniques for Individual 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. While computing the background information similarities, we develop a hybrid similarity model which can deal with different types of properties. The method can solve the data sparseness of U-I rating matrix effectively.

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Periodical:

Advanced Materials Research (Volumes 102-104)

Pages:

31-35

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.102-104.31

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Online since:

March 2010

Authors:

Jian Lan Lei, Jin Wang, Guo Dong Lu, Shao Mei Fei

Keywords:

Collaborative Filtering (CF), Fashion Recommendation, Similarity Computing

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References

[1] T. Joachims, D. Freitag, T. Mitchell, et al: Proceedings of the International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers, (1997), pp.770-777.

Google Scholar

[2] H. Lieberman, N.V. Dyke, and A. Vivacqua, In: Proceedings of the International Conference on Intelligent User Interfaces. Los Angeles, CA: ACM Press, (1999), pp.65-68.

Google Scholar

[3] J. Konstan, B. Miller, D. Maltz, et al: Communications of the ACM, Vol. 40 (1997), pp.77-87.

Google Scholar

[4] U. Shardanand and P. Maes: Proceedings of the ACM CHI'95 Conference on Human Factors in Computing Systems. New York: ACM Press, (1995), pp.210-217.

Google Scholar

[5] D. Goldberg, D. Nichols, B.M. Oki and D. Terry: Communications of the ACM, Vol. 35 (1992).

Google Scholar

[6] Information on http: /www. amazon. com.

Google Scholar

[7] Information on http: /www. hollywoodvideo. com.

Google Scholar

[8] Information on http: /www. musicmatch. com.

Google Scholar

[9] Y.H. Cho and J.K. Kim: Expert Systems with Applications, Vol. 26 (2004), pp.233-246.

Google Scholar

[10] Z.J. Zhang: G for the issue of The Classification of Occupation in the Peoples Republic of China. China Training, (1999).

Google Scholar