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Framework for smoothing-based collaborative filtering recommender system

Published: 29 March 2012 Publication History

Abstract

Picking up a proper product or service is getting more difficult with the increasing number of available products and services on E-business era. Consequently, many companies provide recommending services and many researches have been done in the area. In this paper, we are proposing a framework for smoothing-based collaborative filtering recommender system considering user preferences and product similarities. Primitive experimental results show that the proposed algorithm can provide a better recommendation with user and product information.

References

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Movielens data sets, http://www.grouplens.org/.
[2]
R. Bell and Y. Koren. Improved neighborhood-based collaborative filtering. In KDD Cup and Workshop, 2007.
[3]
J. Bennet and S. Lanning. The netflix prize, www.netflixprize.com, 2007. KDD Cup and Workshop.
[4]
H. Garcia-Molina, G. Koutrika, and A. Parameswaran. Information seeking: Convergence of search, recommendations and advertising. Communications of the ACM, 54:121--130, 2011.
[5]
Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42:8:30--37, 2009.
[6]
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. 10th International conference on the World Wide Web, pages 285--295, 2001.
[7]
X. Su and T. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, Article ID 421425:19 pages, 2009.

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  1. Framework for smoothing-based collaborative filtering recommender system

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    cover image ACM Conferences
    ACMSE '12: Proceedings of the 50th annual ACM Southeast Conference
    March 2012
    424 pages
    ISBN:9781450312035
    DOI:10.1145/2184512
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 March 2012

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    Author Tags

    1. collaborative filtering
    2. hybrid
    3. recommender

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    ACM SE '12
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    ACM SE '12: ACM Southeast Regional Conference
    March 29 - 31, 2012
    Alabama, Tuscaloosa

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    ACMSE '12 Paper Acceptance Rate 28 of 56 submissions, 50%;
    Overall Acceptance Rate 502 of 1,023 submissions, 49%

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