Recommendation systems and social networks Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name:	Recommendation systems and social networks
Module code:	W4-INA-S2-20-F-SRiSS
Programme code:	W4-S2INA19.2021
Semester:	summer semester 2022/2023 winter semester 2022/2023 summer semester 2021/2022
Language of instruction:	English
Form of verification:	course work
ECTS credits:	4
Description:	The module’s aim is to acquaint the students with recommendation systems, their operating principles and algorithms associated with them, and with social networks and methods of their analysis.
Prerequisites:	(no information given)
Key reading:	1. Shlomo Berkovsky, Collaborative Recommendations, World Scientific 2. Neil Wilkins, Artificial Intelligence, 3. Charu C. Aggarwal, Recommender Systems: The Textbook 4. Neil CUNNINGTON, Social Media Algorithm

Learning outcome of the module	Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
The student knows the use and implementation of algorithms used in recommendation systems. [M_001]	K_W01 [1/5] K_W02 [1/5] K_W04 [1/5]
The student knows the operation of recommendation systems and social networks. [M_002]	K_W02 [1/5] K_W05 [1/5] K_W07 [1/5]
The student can choose and implement the algorithm used in recommendation systems. [M_003]	K_U01 [1/5] K_U08 [1/5] K_U09 [1/5]
The student can develop a scheme of dealing with data in recommendation systems, aimed at the proper operation of such a system. [M_004]	K_U03 [1/5] K_U04 [1/5] K_U06 [1/5] K_U09 [1/5]
The student is aware of raising their competences through continuous self-improvement. [M_005]	K_K01 [1/5] K_K02 [1/5]

Type	Description	Codes of the learning outcomes of the module to which assessment is related
Reports [W_001]	The students prepare written reports within a specified period as verification of skills gained during problem-solving.	M_001 M_002 M_003 M_004 M_005
Final test [W_002]	The test checks knowledge on the topics covered in the lectures.	M_001 M_002 M_005

Form of teaching			Student's own work		Assessment of the learning outcomes
Type	Description (including teaching methods)	Number of hours	Description	Number of hours
lecture [Z_001]	The lectures are conducted with multimedia tools and discuss issues related to recommendation systems and social networks.	15	The students prepare for laboratory classes and passing the lecture test.	20	Final test [W_002]
laboratory classes [Z_002]	The classes prepare the students to perform laboratory tasks. They are the practical implementation of issues discussed during lectures.	30	The students prepare for laboratory tasks and solve them individually.	55	Reports [W_001]

Attachments
Module description (PDF)

Information concerning module syllabuses might be changed during studies.

Syllabuses (USOSweb)
Semester	Module	Language of instruction
(no information given)