Description:

Students will learn latest methods and technologies for web data acquisition, analysis and utilization of the discovered knowledge. Students will gain an overview of Web mining techniques for Web crawling, Web structure analysis, Web usage analysis, Web content mining and information extraction. Students will also gain an overview of most recent developments in the field of social web and recommendation systems.

Contents:

1. Key web data mining principles.
2. Web content mining approaches (formats, restrictions, ethical aspects).
3. Web content mining tools.
4. Accessing and extracting specific web content (deep web).
5. Main text mining concepts.
6. Practical applications of text mining.
7. Social network structure and content analysis (2).
8. Web graph, web structure mining.
9. Web usage mining: data collecting.
10. Web usage mining: data analysis, web analytics.
11. Recommender systems and personalization.
12. Data stream mining: algorithms and applications.

Seminar contents:

1. Basics of data acquisition and processing
2. Text preprocessing, text mining applications
3. Acquisition and analysis of graph-based data
4. User data analysis
5. Basics of recommendation systems
6. Project presentation and assessment

Recommended literature:

1. Liu, B. ?Web Data Mining?, Springer-Verlag Berlin Heidelberg, 2011. ISBN 978-3-642-19459-7.
2. Charu C. Aggarwal. ?Machine Learning for Text?, Springer, 2018. ISBN 9783319735313.
3. Easley, D., Kleinberg, J. ?Networks, Crowds, and Markets: Reasoning About a Highly Connected World?, Cambridge
4. A. Russel, M. ?Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (3rd Edition)?, O'Reilly Media, 2019. ISBN 978-1491985045.
5. Charu C. Aggarwal. ?Recommender Systems: The Textbook?, Springer, 2016. ISBN 9783319296579.

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar