Name
Abstract | ||
|---|---|---|
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 χ2 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s00500-006-0115-1 | Soft Comput. |
Keywords | Field | DocType |
model ability,random data set,web usage,full collection,web usage mining ¢ web navigation ¢ variable length markov chain,variable history,markov model,navigation session,training set,last page,predictive power,statistical test,second-order model,test set,mean absolute error,web usage mining,markov chain,second order,web navigation | Data mining,Data set,Web mining,Markov model,Computer science,Markov chain,Artificial intelligence,Web navigation,Variable-order Markov model,Statistical hypothesis testing,Machine learning,Test set | Journal |
Volume | Issue | ISSN |
11 | 8 | 1433-7479 |
Citations | PageRank | References |
4 | 0.46 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Borges | 1 | 144 | 12.93 |
| Mark Levene | 2 | 1272 | 252.84 |