Clustering-based interest computation

Data processing: database and file management or data structures – Database and file access – Post processing of search results

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C706S046000

Reexamination Certificate

active

07979426

ABSTRACT:
Data relating to usage patterns of the user are stored. The data includes a context portion having information as to the context in which items were used and an interest rating portion indicative of an interest of the user in one or more objects of interest. The data is clustered into clusters of data points. For each of the clusters, a centroid is determined. The centroid includes a context portion and an interest rating portion. A current context of the user is received. Clusters are selected by comparing a data point representing the current context with the context portion of one or more centroids. Based on the selected clusters, an interest rating is computed. The computed interest rating indicates an interest of the user in one of the one or more objects of interest, given the current context.

REFERENCES:
patent: 6360227 (2002-03-01), Aggarwal et al.
patent: 6848104 (2005-01-01), Van Ee et al.
patent: 7174343 (2007-02-01), Campos et al.
patent: 7251637 (2007-07-01), Caid et al.
patent: 2001/0014868 (2001-08-01), Herz et al.
patent: 2003/0043815 (2003-03-01), Tinsley et al.
patent: 2004/0220963 (2004-11-01), Chen et al.
patent: 2005/0182852 (2005-08-01), Tinsley et al.
patent: 2005/0234973 (2005-10-01), Zeng et al.
patent: 2007/0174267 (2007-07-01), Patterson et al.
patent: 2007/0271266 (2007-11-01), Acharya et al.
patent: 2009/0063537 (2009-03-01), Bonnefoy-Cudraz et al.
Chen et al., “A Survey of Context-Aware Mobile Computing Research,” Dartmouth Computer Science Technical Report TR2000-381, Dartmouth College, 2000.
Cho et al., “Minimum Sum-Squared Residue Co-clustering of Gene Expression Data”, Proceedings of the Fourth SIAM International Conference on Data Mining (SDM), pp. 114-125, Apr. 2004.
Deshpande et al. “Item-based top-n recommendation algorithms.” In Proc. of IEEE MDM '06, 2006.
Gersho et al. “Vector Quantization and Signal Compression” chapters 2-4, Kluwer Academic Press, 1992.
Herlocker et al., “An Algorithmic Framework for Performing Collaborative Filtering.” In Proc. of SIGIR, 1999.
J.A. Flanagan. “Unsupervised clustering of context data and learning user requirements for a mobile device.” 5thInternational and Interdisciplinary Conference on Modeling and Using context (CONTEXT-05), pp. 155-168, 2005.
J.B. MacQueen. “Some Methods for classification and analysis of multivariate observations.” Proceedings of 5thBerkeley Symposium on Methematical Statistics and Probability, Berkeley, University of California Press, 1:281-297, 1967.
Linde et al. “An algorithm for vector quantizer design.” IEEE Transactions on Communications, vol. Com-28, No. 1, Jan. 1980.
Madeira et al. “Biclustering algorithms for biological data analysis: a survey.” IEEE Transactions on Computational Biology and Bioinformatics, vol. 1, issue 1, pp. 24-45, 2004.
Oku et al. “Context-aware SVM for context-dependent information.” Proceedings of the 7thInternational Conference on Mobile Data Management (MDM '06), 2006.
Rack et al. “Context-aware, ontology-based recommendation.” Proceedings of the International Symposium on Applications and the Internet Workshops (SAINTW '06), 2005.
Ricci et al. “Acquiring and revising preferences in a critique-based mobile recommender system.” IEEE Computer Society, 2007.
Woerndl et al. “A hybrid recommender system for context-aware recommendations of mobile applications.” IEEE 2007.
Woerndl et al. “Utilizing physical and social context to improve recommender systems.” IEEE International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, 2007.
Zhang et al. “Spontaneous and context-aware media recommendation in heterogeneous spaces.” IEEE 2007.
Mobasher et al. “Semantically Enhanced Collaborative Filtering on the Web.” AAI Workshop on Semantic Web Personalization (SWP 2004).
Leung et al. “Applying Cross-Level Association Rule Mining to Cold-Start Recommendation/” Web Intelligence and Intelligent Agent Technology, 2007.
Szomszor et al. “Folksonomies, the Semantic Web, and Movie Recommendations,” 4thEuropean Semantic Web Conference, 2007.
Park et al. “Naïve Filterbots for Robust Cold-Start Recommendations,” KDD 2006.
Good et al. “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” AAAI/IAAI, 1999.
Office Acion in U.S. Appl. No. 12/134,143 dated Nov. 29, 2010.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Clustering-based interest computation does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Clustering-based interest computation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Clustering-based interest computation will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-2662115

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.