Systems and methods for clustering user sessions using...

Details Systems and methods for clustering user sessions using... Systems and methods for clustering user sessions using...

2006-05-09

2006-05-09

LeRoux, Etienne P. (Department: 2161)

Data processing: database and file management or data structures

Database design

Data structure types

Reexamination Certificate

active

07043475

ABSTRACT:
Techniques for clustering user sessions using multi-modal information including proximal cue information are provided. The topology, content and usage of a document collection or web site are determined. User paths are then identified using longest repeating subsequence techniques. An information need feature vector is determined for each significant user path. Further, other feature vectors and proximal cue vectors for each document or web page in the significant path are determined. The other feature vectors include a content feature vector, a uniform resource locator feature vector, an inlink feature vector and an outlink feature vector, among others. The feature vectors and the proximal cue vectors are combined into a multi-modal vector that represents a user profile for each significant user path. The multi-modal vectors are clustered using a type of multi-modal clustering such as K-Means or Wavefront clustering.

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Paper 2001 01 24 SIAM Data mining MMC2.
Ed. H. Chi et al., “Using Information Scent to Model User Information Needs aand Actions on the Web”,SIGCHI'01, Mar. 31-Apr. 4, 2001, Seattle, Washington.
Heer, Jeffrey et al., “Identification of Web User Traffic Composition Using Multi-Modal Clustering and Information Scent”, In proceedings of the Workshop on Web Mining, SIAM Conference on Data Mining, pp. 51-58, Apr. 7, 2001, Chicago, IL.

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