Introduction to recommender systems
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Teaching
Details
Faculty Faculty of Science and Medicine Domain Computer Science Code UE-SIN.08613 Languages English Type of lesson Lecture
Level Master Semester SP-2023 Schedules and rooms
Summary schedule Monday 09:15 - 12:00, Hebdomadaire (Spring semester)
Struct. of the schedule 3h par semaine durant 14 semaines Contact's hours 42 Teaching
Teachers - Teran Tamayo Luis Fernando
Description Recommender systems (RSs) are computer-based techniques that attempt to present information about products that are likely to be of interest to a user. These techniques are mainly used in Electronic Commerce (eCommerce) in order to provide suggestions on items that a customer is, presumably, going to like. Nevertheless, there are other applications that make use of RSs, such as social networks and community-building processes, among others. A recommender system is a specific type of information filtering technique that tries to present users with information about items (movies, music, books, news, web pages, among others) in which they are interested. The term “item” is used to denote what the system recommends to users. To achieve this goal, the user profile is contrasted with the characteristics of the items. These features may come from the item content (content-based approach) or the user’s social environment (CF). The use of these systems is becoming increasingly popular in the Internet because they are very useful to evaluate and filter the vast amount of information available on the Web in order to assist users in their search processes and retrieval. RSs have been highly used and play an important role in different Internet sites that offer products and services in social networks, such as Amazon, YouTube, Netflix, Yahoo!, TripAdvisor, Facebook, and Twitter, among others. Many different companies are developing RSs techniques as an added value to the services they provide to their subscribers.
Training objectives - To understand the basic concepts of RSs
- Using a taxonomy, students will be able to classify different RSs solutions
- To understand a number of RSs algorithms
- To learn about the different evaluation methods for RSsComments MSc-CS BENEFRI - (Code Ue: 53084 Track: T5, Code Ue: 63084 Track: T6) The exact date and time of this course as well as the complete course list can be found at http://mcs.unibnf.ch/.
Course and exam registration on ACADEMIA (not myunifr.ch). Please follow the instructions on https://mcs.unibnf.ch/organization/
Softskills No Off field No BeNeFri Yes Mobility Yes UniPop No -
Dates and rooms
Date Hour Type of lesson Place 20.02.2023 09:15 - 12:00 Cours PER 21, Room C230 27.02.2023 09:15 - 12:00 Cours PER 21, Room C230 06.03.2023 09:15 - 12:00 Cours PER 21, Room C230 13.03.2023 09:15 - 12:00 Cours PER 21, Room C230 20.03.2023 09:15 - 12:00 Cours PER 21, Room C230 27.03.2023 09:15 - 12:00 Cours PER 21, Room C230 03.04.2023 09:15 - 12:00 Cours PER 21, Room C230 17.04.2023 09:15 - 12:00 Cours PER 21, Room C230 24.04.2023 09:15 - 12:00 Cours PER 21, Room C230 01.05.2023 09:15 - 12:00 Cours PER 21, Room C230 08.05.2023 09:15 - 12:00 Cours PER 21, Room C230 15.05.2023 09:15 - 12:00 Cours PER 21, Room C230 22.05.2023 09:15 - 12:00 Cours PER 21, Room C230 -
Assessments methods
Written exam
Assessments methods By rating -
Assignment
Valid for the following curricula: Additional Courses in Sciences
Version: ens_compl_sciences
Paquet indépendant des branches > Specialized courses in Computer Science (Master level)
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