A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Mining Navigation Patterns Using a Sequence Alignment Method
Knowledge and Information Systems
A clickstream-based collaborative filtering personalization model: towards a better performance
Proceedings of the 6th annual ACM international workshop on Web information and data management
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Personalized PageRank for Web Page Prediction Based on Access Time-Length and Frequency
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Predicting WWW surfing using multiple evidence combination
The VLDB Journal — The International Journal on Very Large Data Bases
Integrating recommendation models for improved web page prediction accuracy
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
A Web Usage Mining Approach Based on LCS Algorithm in Online Predicting Recommendation Systems
IV '08 Proceedings of the 2008 12th International Conference Information Visualisation
Web Navigation Prediction Using Multiple Evidence Combination and Domain Knowledge
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
The tremendous progress of the internet and the World Wide Web in the recent era has emphasized the requirement for reducing the latency at the client or the user end. In general, caching and prefetching techniques are used to reduce the delay experienced by the user while waiting to get the web page from the remote web server. The present paper attempts to solve the problem of predicting the next page to be accessed by the user based on the mining of web server logs that maintains the information of users who access the web site. The prediction of next page to be visited by the user may be pre fetched by the browser which in turn reduces the latency for user. Thus analyzing user's past behavior to predict the future web pages to be navigated by the user is of great importance. The proposed model yields good prediction accuracy compared to the existing methods like Markov model, association rule, ANN etc.