Dove e quando

Web usage mining on e-commerce websites Grażyna Suchacka – University of Opole Duration: 8 hours When: 14 – 16 June, 2021 Where: Microsoft Teams PhD credits (DIBRIS metric): 2 PLEASE ENROLL HERE (attendance is FREE): https://docs.google.com/forms/d/1ivAOxr5QpZroTrENtsjEKAg_t5OXfy4nboJLjg… Abstract -------- The course deals with the application of machine learning methods to Web data, in particular in the context of two research problems: detecting Web bots and predicting purchases in online stores. The first problem is due to the presence of artificial agents on the Web which pose a threat to the website security, privacy, and performance. Continuous development of artificial agents’ technology makes Web bot detection, both in the offline and real-time settings, harder and harder. The second problem is connected with discovering various user profiles on e-commerce websites and identifying user sessions with high probability of making a purchase. The problems under consideration are key issues in the era of the rapid development of e-commerce, advanced Web-based technologies, and big data. Program ------- (Italian times, CEST) MONDAY, 14 JUNE 2021 h 14.00 - 16.00 Lecture 1 "Introduction to Web usage mining" - Web usage data - Web usage mining in the context of online stores - Characteristics and differences in bot and human Web traffic - Data pre-processing for Web usage mining, reconstruction of user sessions TUESDAY, 15 JUNE 2021 h 11.00 - 13.00 Lecture 2 "Web bot detection, part 1" - Problems of offline and online bot detection - Feature selection and feature extraction for bot detection - Offline bot detection with supervised classification methods TUESDAY, 15 JUNE 2021 h 14.00 - 16.00 Lecture 3 "Web bot detection, part 2" - Offline bot detection with unsupervised classification methods - Online bot detection WEDNESDAY, 16 JUNE 2021 h 11.00 - 13.00 Lecture 4 "Online purchase prediction" - Problem of predicting online purchases - Feature selection for online purchase prediction - Purchase prediction with machine learning methods