Seminar hands-on recommender systems
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Teaching
Details
Faculty Faculty of Science and Medicine Domain Computer Science Code UE-SIN.07827 Languages English Type of lesson Seminar
Level Master Semester SA-2022 Schedules and rooms
Struct. of the schedule 2h par semaine durant 14 semaines Contact's hours 28 Teaching
Responsibles - Teran Tamayo Luis Fernando
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 (Collaborative Filtering). 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 subscribers.
Training objectives The objective of this seminar is that students will be able to develop a recommender system solution.
Comments MSc-CS BENEFRI - (Code Ue: 53511 / Tracks: T5, Code Ue: 63511/ Tracks: 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 -
Assessments methods
Evaluation continue
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)
Additional programme requirements for PhD studies [PRE-DOC]
Version: 2020_1/v_01
Additional programme requirements for PhD studies (Faculty of Science and Medicine) > Specialized courses in Computer Science (Master level)
Computer Science [3e cycle]
Version: 2015_1/V_01
Continuing education > Specialized courses in Computer Science (Master level)
Computer Science [POST-DOC]
Version: 2015_1/V_01
Continuing education > Specialized courses in Computer Science (Master level)
MSc in Computer science (BeNeFri)
Version: 2023_1/V_01
MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > T5 : Information Systems and Decision SupportMSc in Computer science (BeNeFri), lectures, seminars and Master thesis > T6: Data Science