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US007603331B2

(12) United States Patent

Tuzhilin et al.

(io) Patent No.: (45) Date of Patent:

US 7,603,331 B2 Oct. 13, 2009

(54) SYSTEM AND METHOD FOR DYNAMIC
PROFILING OF USERS IN ONE-TO-ONE
APPLICATIONS AND FOR VALIDATING
USER RULES

(75) Inventors: Alexander S. Tuzhilin, New York, NY (US); Gediminas Adomavicius, Jersey City, NJ (US)

(73) Assignee: New York University, New York, NY (US)

( * ) Notice: Subject to any disclaimer, the term of this patent is extended or adjusted under 35 U.S.C. 154(b) by 360 days.

(21) Appl.No.: 11/074,157

(22) Filed: Mar. 7, 2005

(65) Prior Publication Data

US 2005/0149460 Al Jul. 7, 2005

Related U.S. Application Data

(63) Continuation of application No. 09/554,383, filed as application No. PCT/US98/24339 on Nov. 13, 1998, now Pat. No. 6,871,186, which is a continuation-inpart of application No. 08/970,359, filed on Nov. 14, 1997, now Pat. No. 6,236,978.

(51) Int. CI.

G06F17/00 (2006.01)
G06N 5/00 (2006.01)

(52) U.S. CI 706/45; 706/48

(58) Field of Classification Search 705/10

See application file for complete search history.

(56) References Cited

U.S. PATENT DOCUMENTS 4,775,935 A 10/1988 Yourick

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A system and method for generating and validating a user profile (25) for a user based on a static profile (10) and a dynamic profile (15) of the user. The method and system compresses the dynamic rules (15) into aggregated rules so that the user can view a comparatively small number of the aggregated rules and select the desired rules from the aggregated rules based on user desired criteria. The method and system validates user rules (60) using a processing device, which are retrieved from a storage device. The user rules are separated into at least one subset of a user set. Then, it is determined if a particular rule of the at least one subset is one of acceptable, unacceptable and undecided based on a defined criteria (415). If the particular rules of the at least one subset are acceptable, the particular rules of the at least one subset are provided (e.g. assigned) to a corresponding user (435).

59 Claims, 16 Drawing Sheets

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"Creating a New Medium for Marketing and Selling", Broad Vision, May 10, 1997.

"Firefly Ships Internet's First Solaris Solution for Managing Personalized Relationsuips with Users Online while Protecting their Privacy", Firefly Press Release, Oct. 14, 1997.

Engage Technologies, a Subsidiary of DMG Information Services, Inc., Launches a Suite of Advanced Enabling Technologies for Accelerated, One-to_one Web Marketing, Engage Technologies, Inc., Press Release 1997.

C. Shum et al., "Implicit Representation for Extensional Answers", Expert Database Systems, Benjamin/Cummings Publishing Co., Inc., International Conference on Expert Database Systems, 1988, pp. 497-521.

H. Zimmermann, "Fuzzy Set Theory—and Its Applications", Kluwer-Nijhoff Publishing, pp. 11-22.

J. Quinlan, "C4.5: Programs for Machine Learning", Morgan Kaufmann Publishers, pp. 1-54.

L. Breiman et al., "Classification and Regression Trees", Wadsworth International Group, pp. 18-27.

A. Jhingran, "Data Mining and E-Commerce", IBM TJ Watson Research Center, Oct. 1997.

T. Fawcett et al., "Combining Data Mining and Machine Learning for

Effective User Profiling", NYNEX Science and Technology.

"Engage Suite of Products", Engage Technologies.

M. Tucker, "Dough". Datamation, May 1997, pp. 51-58.

R. Cooley et al., "Grouping Web Page Reference into Transactions

for Mining World Wide Web Browsing Patterns", Knowledge and

Data Engineering Exchange Workshop, 1997, Proceedings, pp. 2-9.

Jesus Cerquides et al., "Fuzzy Metaqueries for Guiding the Discovery Process in KDD", Fuzzy Systems, 1997, vol. 3, Proceedings of the Sixth IEEE International Conference, pp. 1555-1559.

A. I. Kokkinaki, "On Atypical Database Transactions: Identification of Probable Frauds Using Machine Learning For User Profiling", Knowledge and Data Engineering Workshop, 1997, Proceedings, pp. 107-113.

Mike Hogan, Sattellites, Radio, and Super Wireless for New High-
Speed Net Access:, PC World, Oct. 1997, p. 68-72.
Ch. Dujet et al., About Modus Ponens and Aggregation of Rules;,
Fuzzy Systems, Mar. 1995, p. 1825-1832.

Rakesh Agrawal et al., "Database Mining A Performance Perspective", IEEE Transactions on Knowledge and Data Engineering, Dec. 1993, p. 914-925.

Eutani Kim et al., "A New Approach to Fuzzy Modeling", IEEE

Transactions on Fuzzy Systems, Aug. 1997, p. 3428-337.

C.B. Kappert et al., "Neural Nerworks and Business Modelling-An

Application of Neural Modelling Techniques to Prospect Profiling in

the Telecommunications Industry", System Sciences, Jan. 1997, p.

465-473.

Mika Klemettinen et al., "Finding Interesting Rules from Large Sets of Discovered Association Rules", ACM, Nov. 1994, p. 401-407. R.R. Yager, "Fuzzy Summaries in Database Mining", Artificial Intelligence for Applications, Feb. 1995, p. 265- 269. T. Fawcett et al., "Adaptive Fraud Detection", Data Mining and Knowledge Discovery, vol. I, No. 3, Nov. 1987, pp. 291-316.

B. Lent et al., Clustering Association Rules:, Proc. of 13th International Conference on Data Engineering, Apr. 1997, U.K., pp. 1-19. M. Pazzani et al., "Syskill & Webert: Identifying Interesting web sites", Proceedings of the National Conference on Artificial Intelligence, 1996.

M. Pazzani et al., "Learning and Revising User Profiles: The Identification of Interesting Web Sites", Machine Learning, 27, 1997, pp. 313-331.

Bruce Krulwich, "Lifestyle Finder: Intelligent User Profiling Using Large-Scale Demographic Data", Al Magazine, Summer 1997, p. 37-45.

K.L. Wu et al. "Speedtracer: A Web usage mining and tool", IBM

Systems Journal, vol. 37, No. 1, 1998, p. 89-105.

Beverly Cramp, "Reading Your Mind", Marketing, Feb. 22, 1996 p.

33-34.

Imielinski et al., "A Database Perspective on Knowledge Discovery", Communications of the ACM, Nov. 1996, v39, nl 1, p. 58(7). Szladow et al., "Rough sets: Working with Imperfect Data", Al Expert, Jul. 1993, v8, n7, p. 36(6).

Amihai Motro, "Using Integrity Constraints to Provide Intensional Answers to Relational Queries", Computer Science Dept. Univ. Of South California, Proceedings of the Fifteenth International Conference on Very Large Data Bases, Amsterdam, 1989, p. 237-245. S.H. Clearwater et al., "RL4: A Tool for Knowledge-Based Induction", Dept. Of Computer Science, Univ. Of Pittsburgh, IEEE 1990, 24-30.

* cited by examiner

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