It would seem, that web site user behavior is highly unique and different from other users behavior. It is based on user current intention and previous experiences with the web site. But the web site itself offers only finite number of possibilities, in which users can behave. Thanks to this fact, we can find users, who behave similarly. Then, we can use this information in tasks like personalization, user modeling, recommendation or prediction.
In our work, we analyze possibilities of user behavior clustering. Because we work with a lot of data in web sites with dynamically changing content, we focus on clustering in data stream. We are solving subtasks like feature engineering, distance and cluster quality measurements. Then we want to use these obtained clusters / behavior similarities to improve task of recommendation. At the end, we want to test our method on nontrivial real dataset and show, that clustering can help to get better results for task of recommendation.
Personalized Web Research Group
Faculty of Informatics and Information Technologies
Slovak University of Technology in Bratislava