Testing the predictive power of variable history web usage

• Type: Article in International Scientific Journal
• Date: 2007

Authors

José Luís Moura Borges (Author) FEUP; Levene, M (Author) Other

Journal

Title: Soft Computing

Vol. 11 No. 8

Pages: 717-727

ISSN: 1432-7643

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-004-9Z0

DOI: 10.1007/s00500-006-0115-1

Abstract (EN): We present two methods for testing the predictive power of a variable length Markov chain induced from a collection of user web navigation sessions. The collection of sessions is split into a training and a test set. The first method uses a chi(2) statistical test to measure the significance of the distance between the distribution of the probabilities assigned to the test trails by a Markov model build from the full collection of sessions and a model built from the training set. The statistical test measures the ability of the model to generalise its predictions to the unseen sessions from the test set. The second method evaluates the model ability to predict the last page of a navigation session based on the preceding pages viewed by recording the mean absolute error of the rank of the last occurring page among the predictions provided by the model. Experimental results conducted on both real and random data sets are reported and the results show that in most cases a second-order model is able to capture sufficient history to predict the next link choice with high accuracy.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11