Discovering task-oriented usage pattern for web recommendation
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Web Co-clustering of Usage Network Using Tensor Decomposition
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Web user profiling on proxy logs and its evaluation in personalization
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Topic analysis of web user behavior using LDA model on proxy logs
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Towards user profiling for web recommendation
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A web recommendation technique based on probabilistic latent semantic analysis
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
A latent usage approach for clustering web transaction and building user profile
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Structure based semantic measurement for information filtering agents
AOW '07 Proceedings of the Third Australasian Workshop on Advances in Ontologies - Volume 85
| Hi-index | 0.00 |
The locality of web pages within a web site is initially determined by the designerýs expectation. Web usage mining can discover the patterns in the navigational behaviour of web visitors, in turn, improve web site functionality and service designing by considering usersý actual opinion. Conventional web page clustering technique is often utilized to reveal the functional similarity of web pages. However, high-dimensional computation problem will be incurred due to taking user transaction as dimension. In this paper, we propose a new web page grouping approach based on Probabilistic Latent Semantic Analysis (PLSA) model. An iterative algorithm based on maximum likelihood principle is employed to overcome the aforementioned computational shortcoming. The web pages are classified into various groups according to user access patterns. Meanwhile, the semantic latent factors or tasks are characterized by extracting the content of "dominant" pages related to the factors. We demonstrate the effectiveness of our approach by conducting experiments on real world data sets.