Prediction of Web Browsing Behavior based on Sequential Data Mining

10.7903/ijecs.2061

Li-Ching Ma；Pei-Pei Hsu

Data mining ； prediction ； web browsing behavior ； sequential data mining ； web recommendation

International Journal of Electronic Commerce Studies

13卷3期（2022 / 09 / 01）

1 - 19

英文

Discovering time-related transaction behavior or patterns is helpful for businesses in suggesting appropriate products to their customers. For web systems, it is important to understand customers' browsing behavior to design or recommend products or services that customers need. This study proposes an approach for predicting web browsing behavior that integrates the concepts of sequential data mining, Borda majority count, bit-string operation, and PrefixSpan algorithm. By incorporating the concept of Borda majority count and sequential data mining, the proposed approach can discover majority-based priorities of items for recommendation and improve prediction accuracy. In addition, the proposed approach employs the concept of bit-string operation and the PrefixSpan algorithm to increase computational efficiency. This research employs the concept of ensemble methods that combine multiple models to derive improved results. Compared to previous methods, the proposed approach can yield higher prediction accuracy. Moreover, the proposed approach can provide flexibility for decision-makers in adjusting a minimum support level and the number of items for recommendation. The proposed approach can also be applied to many fields.

1. Agrawal, R.,Srikant, R.(1995).Mining sequential pattern.Proc. 11th Int. Conf. on Data Engineering,Taipei, Taiwan:
2. Agrawal, R.,Srikant, R.(1994).Fast algorithms for mining association rules.Proc 20th Int. Conf. on VLDB,Santiago de Chile, Chile:
3. Borda, J. C.(1995).Mémoire sur les élections au scrutiny.Classics of Social Choice,Ann Arbor:
4. Burke, R.(2001).Hybrid recommender systems: survey and experiments.User Modeling and User-Adapted Interaction,12,331-370.
5. Chen, Y. L.,Cheng, L. C.(2009).Mining maximum consensus sequences from group ranking data.European Journal of Operational Research,198,241-251.
6. Chen, Y. L.,Hu, Y. H.(2006).Constraint-based sequential pattern mining: the consideration of recency and compactness.Decision Support Systems,42,1203-1215.
7. Cheung, K. W.,Kwok, J. T.,Law, M. H.,Tsui, K. C.(2003).Mining customer product ratings for personalized marketing.Decision Support Systems,35(2),231-243.
8. Choi, K.,Yoo, D.,Kim, G.,Suh, Y.(2012).A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis.Electronic Commerce Research and Applications,11,309-317.
9. Desai, N. A.,Ganatra, A.(2015).Buying scenario and recommendation of purchase by constraint based sequential pattern mining from time stamp based sequential dataset.Procedia Computer Science,45,166-175.
10. Hu, Y. H.,Wu, F.,Liao, Y. J.(2013).An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports.The Journal of Systems and Software,86(5),1224-1238.
11. Liao, V. C. C.,Chen, M. S.(2014).Dfsp: a depth-first spelling algorithm for sequential pattern mining of biological sequence.Knowledge and Information Systems,38(3),623-639.
12. Ma, L. C.(2018).Discovering consensus preferences visually based on Gower plots.International Journal of Information Technology & Decision Making,17(3),741-761.
13. Ma, L. C.(2019).A new consensus mining approach to group ranking problems involving different intensities of preferences.Computers & Industrial Engineering,131,320-326.
14. Ma, L. C.(2016).A new group ranking approach for ordinal preferences based on group maximum consensus sequences.European Journal of Operational Research,251(1),171-181.
15. Mishra, R.,Kumar, P.(2012).Clustering web logs using similarity upper approximation with different similarity measures.International Journal of Machine Learning and Computing,2(3),219-221.
16. Mishra, R.,Kumar, P.,Bhasker, B.(2015).A web recommendation system considering sequential information.Decision Support Systems,75,1-10.
17. Pazzani, M.,Billsus, D.(1997).Learning and revising user profile: the identification of interesting web sites.Machine Learning,27(3),313-331.
18. Pei, J.,Han, J.,Mortazavi-Asl, B.,Pinto, H.,Chen, Q.,Dayal, U.,Hsu, M. C.(2001).PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth.Proc. of Int. Conf. on Data Engineering,Heidelberg, Germany:
19. Salehi, M.,Kamalabadi, I. N.(2013).Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner’s preference tree.Knowledge-Based Systems,48,57-69.
20. SenKul, P.,Salin, S.(2012).Improving pattern quality in web usage mining by using semantic information.Knowledge and Information Systems,30(3),527-541.
21. Shyur, H. J.,Jou, C.,Chang, K.(2013).A data mining approach to discovering reliable sequential patterns.The Journal of Systems and Software,86,2196-2203.
22. Wang, Y.,Dai, W.,Yuan, Y.(2008).Website browsing aid: a navigation graph-based recommendation system.Decision Support Systems,45(3),387-400.
23. Wright, A. P.,Wright, A. T.,McCoy, A. B.,Sittig,D. F.(2015).The use of sequential pattern mining to predict next prescribed medications.Journal of Biomedical Informatics,53,73-80.
24. Yen, S. J.,Lee, Y. S.(2006).An efficient data mining approach for discovering interesting knowledge from customer transactions.Expert Systems with Applications,230(4),650-657.
25. Yu, K.,Schwaighofer, A.,Tresp, V.,Xu, X.,Kriegel, H. P.(2004).Probabilistic memory-based collaborative filtering.IEEE Transactions on Knowledge and Data Engineering,16(1),56-69.
26. Zahid, M. A.,Swart, H. D.(2015).The Borda majority count.Information Sciences,295,429-440.
27. Zhang, Z.,Liu, Y.,Ding, W.,Huang, W.,Su, Q.,Chen, P.(2015).Proposing a new friend recommendation method, FRUTAI, to enhance social media providers’ performance.Decision Support Systems,79,46-54.