Abstract
In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.
| Original language | English |
|---|---|
| Pages (from-to) | 579-597 |
| Number of pages | 19 |
| Journal | Soft Computing |
| Volume | 14 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Apr 2010 |
Funding
This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) EEEAG project 105E162. The authors would like to thank Murat Göksedef for his help in the recommendation engine and H. Turgut Uyar for his useful suggestions and careful reading of the manuscript.
| Funders | Funder number |
|---|---|
| TUBITAK | 105E162 |
| Türkiye Bilimsel ve Teknolojik Araştirma Kurumu |
Keywords
- Graph clustering
- Multiobjective clustering
- Multiobjective evolutionary algorithms
- Sequence clustering
- User session clustering