USERS INTEREST PREDICTION MODEL - Based on 2nd Markov Model and Inter-transaction Association Rules

Yonggong Ren, Alma Leora Culén

Abstract

The 2nd Markov Model and inter-transaction association rules are both known as key technologies for building user interest prediction models. The use of these technologies potentially improves the users surfing experience. The use of the 2nd Markov Model increases the accuracy of predictions, but it does not cover all the data. Therefore, in this paper we propose a dual strategy for a user interest prediction model that includes the entire data set and improves the accuracy of inter-transaction association rules. The foundation of our dual strategy is a new method of building a database based on the degree of user interest. Secondly, we integrate the 2nd Markov Model and inter-transaction association rules for predicting future browsing patterns of users. Experimental results show that this method provides more accurate prediction results than previous similar research.

References

  1. Chen, J. et al., 2004. Discovering Web usage patterns by mining cross-transaction association rules. In Proceedings of the International Conference on Machine Learning and Cybernetics - Volume 5, pp. 2655- 2660
  2. Chen, J. and Liu, W., 2006. Research for Web Usage Mining Model. In Proceedings of International Conference on Intelligent Agents Web Technologies and International Commerce, pp. 8-8.
  3. Chimphlee, S. et al., 2006. Using Association Rules and Markov Model for Predit Next Access on Web Usage Mining. In T. Sobh & K. Elleithy, eds. Advances in Systems, Computing Sciences and Software Engineering. Dordrecht: Springer Netherlands, pp. 371-376.
  4. Chimphlee, S. et al., 2006. Rough Sets Clustering and Markov model for Web Access Prediction. In Proceedings of the Postgraduate Annual Research Seminar, pp.470-476.
  5. Deshpande, M. & Karypis, G., 2004. Selective Markov models for predicting Web page accesses. In ACM Transactions on Internet Technology, Volume 4, pp.163-184.
  6. Khalil, F., Li, J. & Wang, H., 2008. Integrating recommendation models for improved web page prediction accuracy. In Proceedings of the thirty-first Australasian conference on Computer science - Volume 74, pp. 91-100.
  7. Khalil, F., Li, J. & Wang, H., 2006. A framework of combining Markov model with association rules for predicting web page accesses. In Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61, pp. 177-184.
  8. Mobasher, B. et al., 2002. Using sequential and nonsequential patterns in predictive Web usage mining tasks. In Proceedings of the IEEE International Conference on Data Mining, pp.669- 672.
  9. Mobasher, B. et al., 2001. Effective personalization based on association rule discovery from web usage data. In Proceedings of the 3rd international workshop on Web information and data management, pp. 9-15.
  10. Ren, Y., & Culén, A. L., 2009. Clustering Based on Data Attribute Partition and Its Visualization. In Proceedings of the Second International Conferences on Advances in Computer-Human Interactions, pp. 13-18.
  11. Tung, A. K. H. et al., 1999. Breaking the barrier of transactions: mining inter-transaction association rules. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 297-301.
Download


Paper Citation


in Harvard Style

Ren Y. and Culén A. (2011). USERS INTEREST PREDICTION MODEL - Based on 2nd Markov Model and Inter-transaction Association Rules . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 236-241. DOI: 10.5220/0003659502440249


in Bibtex Style

@conference{kdir11,
author={Yonggong Ren and Alma Leora Culén},
title={USERS INTEREST PREDICTION MODEL - Based on 2nd Markov Model and Inter-transaction Association Rules},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={236-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003659502440249},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - USERS INTEREST PREDICTION MODEL - Based on 2nd Markov Model and Inter-transaction Association Rules
SN - 978-989-8425-79-9
AU - Ren Y.
AU - Culén A.
PY - 2011
SP - 236
EP - 241
DO - 10.5220/0003659502440249