Systems and methods for identifying user types using...

Details Systems and methods for identifying user types using... Systems and methods for identifying user types using...

2007-08-21

2007-08-21

Jaroenchonwanit, Bunjob (Department: 2152)

Electrical computers and digital processing systems: multicomput

Computer-to-computer data routing

C709S218000, C709S223000

Reexamination Certificate

active

09820988

ABSTRACT:
Techniques for determining user types based on multi-modal clustering are provided. The topology, content and usage of a document collection or web site is determined. The user paths are identified using longest repeating subsequence techniques and a multi-modal information need vector is determined for each significant user path. Multi-modal vectors for each document in the significant path, content, uniform resource locators, inlink and outlink multi-modal vectors are determined and combined based on path position and access frequency. Multi-modal clustering is performed based on a multi-modal similarity function and a specified measure of similarity using a type of multi-modal clustering such as K-means or wavefront clustering. The identified clusters may be further analyzed based on changes to the weighting of the corresponding content, url, inlinks and outlinks multi-modal feature vectors.

REFERENCES:
patent: 5835905 (1998-11-01), Pirolli et al.
patent: 5878384 (1999-03-01), Johnson et al.
patent: 6029195 (2000-02-01), Herz
patent: 6067565 (2000-05-01), Horvitz
patent: 6154767 (2000-11-01), Altschuler et al.
patent: 6167397 (2000-12-01), Jacobson et al.
patent: 6356898 (2002-03-01), Cohen et al.
patent: 6385641 (2002-05-01), Jiang et al.
patent: 6421733 (2002-07-01), Tso et al.
patent: 6430558 (2002-08-01), Delano
patent: 6581072 (2003-06-01), Mathur et al.
patent: 6622168 (2003-09-01), Datta
patent: 6681247 (2004-01-01), Payton
patent: 6728932 (2004-04-01), Chundi et al.
patent: 6982708 (2006-01-01), Mah et al.
patent: 7010527 (2006-03-01), Alpha
patent: 7017110 (2006-03-01), Chi et al.
patent: 7020643 (2006-03-01), Mah et al.
patent: 7028053 (2006-04-01), Chi et al.
patent: 7031961 (2006-04-01), Pitkow et al.
patent: 7039642 (2006-05-01), Horvitz et al.
patent: 7043475 (2006-05-01), Heer et al.
patent: 7043535 (2006-05-01), Chi et al.
patent: 7043702 (2006-05-01), Chi et al.
patent: 7194466 (2007-03-01), Chen et al.
patent: 7203899 (2007-04-01), Chi et al.
patent: 2003/0167443 (2003-09-01), Meunier et al.
patent: 2005/0144067 (2005-06-01), Farahat et al.
Pitkow, James and Pirolli, Peter, “Mining Longest Repeating Subsequences to Predict World Wide Web Surfing,” The 2ndUSENIX Symposium on Internet Technologies & Systems, The USENIX Association, Oct. 1999.
Yuwono, Budi; Lee, Dik L., “Search and Ranking Algorithms for Locating Resources on the World Wide Web,” Proceedings of the Twelfth International Conference on Data Engineering, 1996, Feb. 26 through Mar. 1, 1996, pp. 164-171.
Picard, Justin, “Modeling and Combining Evidence Provided by Document Relationships Using Probabilistic Argumentation Systems,” Proceedings of the 21stAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug. 1998, pp. 182-189.
Craswell, Nick; Hawking, David; Robertson, Stephen, “Effective Site Finding Using Link Anchor Information,” Proceedings of the 24thAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sep. 2001, pp. 250-257.
B. A. Huberman, P. Pirolli, J. Pitkow and R. J. Lukose (1998). Strong Regularities in World Wide Web Surfing. Science 280: 95-97.
Ed H. Chi, Peter Pirolli, James Pitkow. The Scent of a Site: A System for Analyzing and Predicting Information Scent, Usage, and Usability of a Web Site. In Proc. Of ACM CHI 2000 Conference on Human Factors in Systems, pp. 161-168, 581-582. ACM Press, 2000. Amsterdam, Netherlands.
P. Pirolli and J. E. Pitkow (1999). Distributions of Surfers' Paths Through the World Wide Web: Empirical Characterization. World Wide Web 2(1-2): 29-45.
J. E. Pitkow and P. Pirolli (1999). Mining longest repeated subsequences to predict World Wide Web surfing. Second USENIX Symposium on Internet Technologies and Systems.
System And Method For Information Browsing Using Multi-Modal Features. Francine R. Chen, Hinrich Scheutze, Ullas Gargi. Oct. 19, 1999.
System And Method For Providing Recommendations Based on Multi-Modal User Clusters, Hinrich Schuetze, James E. Pitkow, Peter L. Pirolli, Ed H. Chi, Jun Li Oct. 19, 1999.
System And Method For Quantitatively Representing Data Objects In Vector Space. Hinrich Scheutze, Francine R. Chen, Peter Pirolli, James E. Pitkow, Ed H. Chi, Jun Li, Ullas Gargi Oct. 19, 1999.
System And Method For Identifying Similarities Among Documents In A Collection. Hinrich Schuetze, Francine R. Chen, Peter L. Pirolli, James E. Pitkow, Ed H. Chi.
System And Method For Clustering Data Objects In A Collection. Hinrich Schuetze, Peter L. Pirolli, James E. Pitkow, Ed H. Chi, Jun Li Oct. 19, 1999.
System And Method For Visually Representing The Contents Of A Multiple Data Object Cluster. Hinrich Schuetze, Peter L. Pirolli, James E. Pitkow, Ed H. Chi, Jun Li Oct. 19, 1999.
System And Method For Inferring User Information Need In A Hyermedia Linked Document Collection. Ed H. Chi et al.
Chi et al.: “The Scent of a Site: A System for Analyzing and Predicting Information Scent, Usage, and Usability of a Web Site,” Chi 2000 Conference Proceedings, Conference on Human Factors in Computing Systems, Apr. 1, 2000, pp. 161-168.
Cetintemel et al.: “Self-adaptive User Profiles for Largo-Scale Data Delivery,” Data Engineering 2000, 16thInternational Conference in San Diego CA USA, Feb. 29, 2000, pp. 622-633.
Chen et al.: “Bringing Order to the Web: Automatically categorizing Scarch Results,” Chi 2000 Conference, Conference on Proceedings Human Factors in Computing Systems, Apr. 1, 2000, pp. 145152.
Heer et al.: “Identification of Web User Traffic Composition Using Multi-Modal Clustering and Information Scent,” Internet Article, Proceedings of the Workshop on Web Mining, First SIAM International Conference on Data Mining, Apr. 5, 2001, pp. 1-13.
System And Method For identifying Similarities Among Objects In A Collection. Hinrich Schuetze, Francine R. Chen, Peter L. Pirolli, James E. Pitkow, Ed H. Chi, Oct. 19, 1999.

Affiliated with

Also associated with

Rating

Systems and methods for identifying user types using... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Systems and methods for identifying user types using..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Systems and methods for identifying user types using... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3892378