Recommendation diversity

Data processing: database and file management or data structures – Database and file access – Preparing data for information retrieval

Reexamination Certificate

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Details

C707S758000, C707S916000, C707S957000

Reexamination Certificate

active

07860862

ABSTRACT:
Recommendation systems and methods are disclosed that objectively determine similarities between products and quantify diversity between products for use in generating recommendations. The product interests, such as musical interests, of a user are measured based on objective characteristics of the product. Then the interests are modeled by a distribution. The resulting distribution is then used as a measure of the diversity of the user's tastes. Based on the diversity and the characteristics of other products, recommendations are then made to the user. The systems and methods may also utilize subjective information as a secondary filter to add or remove products for which such data is known.

REFERENCES:
patent: 7013238 (2006-03-01), Weare
patent: 7283137 (2007-10-01), Suyama et al.
patent: 2002/0082901 (2002-06-01), Dunning et al.
patent: 2003/0014407 (2003-01-01), Blatter et al.
patent: 2005/0276485 (2005-12-01), Mori et al.
patent: 2006/0173910 (2006-08-01), McLaughlin
Bradley et al. “Improving Recommendation Diversity” In D. O'Donoghue, editor, Proceedings of the Twelfth National Conference in Artificial Intelligence and Cognitive Science (AICS-01), Maynooth, Ireland, pp. 75-84, 2001.
Chang et al. “LIBSVM—A Library for Support Vector Machines” http://www.csie.ntu.edu.tw/˜cjlin/libsvm/ Sep. 2, 2006.
Duchene et al. “An Optimal Transformation for Disciminant and Principal Component Analysis” IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 10, No, 6 Nov. 1988.
Kleinberg et al. “Using Mixture Models for Collaborative Filtering” Annual ACM Symposium on Theory of Computing archive Proceedings of the thirty-sixth annual ACM symposium on Theory of computing table of contents Chicago, IL, USA, 2004, pp. 569-578.
Logan et al. “Toward Evaluation Techniques for Music Similarity” Published in and Presented at SIGIR 2003: Workshop on the Evaluation of Music Information Retrieval Systems, Aug. 1, 2003, Toronto, Canada.
Logan et al. “Music Summarization Using Key Phrases” International Conference on Acoustics, Speech, and Signal Processing, 2000. Jun. 5, 2000-Jun. 9, 2000, Publication Date: 2000, Vol. 2, on pp. II749-II752 vol. 2; Location: Istanbul, Turkey.
Pampalk et al. “Improvements of Audio-Based Music Similarity and Genre Classification” In Proceedings of the Sixth International Conference on Music Information Retrieval (ISMIR) 2005.
Platt et al. “Learning a Gaussian Process Prior for Automatically Generating Music Playlists” Advances in Neural Information Processing Systems, 2002-research.microsoft.com.
Tzanetakis et al. “MARSYAS: A Framework for Audio Analysis” Organized Sound (1999), 4: 169-175 Cambridge University Press.
Tzanetakis, G. “Musical Genre Classification of Audio Signals” IEEE Transactions on Speech and Audio Processing, vol. 10, No. 5, Jul. 2002, 293-302.
Zhai et al. “A Risk Minimization Framework for Information Retrieval” Information Processing and Management (IP&M), 42(1), Jan. 2006, pp. 31-35.
Ziegler et al. “Improving Recommendation Lists Through Topic Diversification” WWW 2005, May 10-14 2005, Chiba Japan.
U.S. Appl. No. 11/255,524, Ranasinghe et al.
U.S. Appl. No. 11/250,359, Kindig.
U.S. Appl. No. 11/250,358, Kindig.

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