PV202 Service Systems LaboratoryFaculty of Informatics
- Extent and Intensity
- 0/0/2. 2 credit(s). Recommended Type of Completion: k (colloquium). Other types of completion: z (credit).
- Ing. Leonard Walletzký, Ph.D. (lecturer)
Mgr. Jitka Kitner (seminar tutor)
- Guaranteed by
- doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications - Faculty of Informatics
Supplier department: Department of Computer Systems and Communications - Faculty of Informatics
- Wed 12. 10. 12:00–15:50 B517, 18:00–19:50 A218, Thu 13. 10. 12:00–15:50 B517, 18:00–19:50 A217, Fri 14. 10. 10:00–13:50 A217, 14:00–15:50 A217
- PB114 Data Modelling I && SOUHLAS
Preconditions for this course: (1) English; (2) In the seminar, the students are expected to develop their own recommender system project. It will involve some web development, algorithm implementation and system design.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20
- fields of study / plans the course is directly associated with
- there are 37 fields of study the course is directly associated with, display
- Course objectives
- Course objective: The course of Recommender System for Service Science is to present the knowledge of recommender systems in the context of Service Science. The students will learn algorithms, mathematical underpinnings and up-to-date research results in recommender systems and information retrieval. The course will also provide several real-world recommender system applications such as AMAZON recommendation and booking.com recommendation to students. The students will learn and discuss the applications according to the case studies in the context of Service Science. In the seminar, the students are expected to design and try to develop a system prototype and present their work in recommender systems.
- The lecturer Mouzhi Ge will explain further topics from Recommender System for Service Science, e.g. Construct a recommender for Cloud IT service:
- Introduction to Recommender Systems
- Collaborative filtering, Content-based and Knowledge-based recommendations
- Explanation in recommender systems
- Recommender System and Service Science
- Group Recommendations
- Evaluating recommender systems
- Case study – personalized recommendations on the Internet
- Recommender systems and the next-generation Web
- Recommendations in ubiquitous environments
- Context-aware recommender system
- Recommender system and HCI
- Teaching methods
- lectures, making a quick RecSys prototype
- Assessment methods
- In the seminar, the students are expected to develop their own recommender system project. It will involve some web development, algorithm implementation and system design. The students can choose the domain they like, the domain should be related to Service Science. As the block course is short, the students should at least design the system, if some students have ever done the web development before (e.g. they don’t need time to learn how to set up a server, install an IDE).
- Language of instruction
- Further comments (probably available only in Czech)
- Study Materials
The course is taught each semester.
- Teacher's information