[DBWorld] Reminder - Course: "Web usage mining on e-commerce websites"

COURSE ANNOUNCEMENT

Web usage mining on e-commerce websites

Gra=C5=BCyna Suchacka =E2=80=93 University of Opole


Duration:  8 hours
When: 14 =E2=80=93 16 June, 2021 =

Where: Microsoft Teams
PhD credits (DIBRIS metric): 2

PLEASE ENROL HERE (attendance is FREE):
https://docs.google.com/forms/d/e/1FAIpQLSf8jfnT-ITB9ujI8OY2AOPJeoRBkUb7X4O=
3T9HQVDLe0S9LNQ/viewform?usp=3Dsf_link


Abstract
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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=E2=80=99 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
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(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

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