Introduction to recommender systems
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Enseignement
Détails
Faculté Faculté des sciences et de médecine Domaine Informatique Code UE-SIN.08613 Langues Anglais Type d'enseignement Cours
Cursus Master Semestre(s) SP-2022 Horaires et salles
Horaire résumé Lundi 09:15 - 12:00, Hebdomadaire (Semestre de printemps)
Struct. des horaires 3h par semaine durant 14 semaines Heures de contact 42 Enseignement
Responsables - Portmann Edy
Enseignants - 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.
Objectifs de formation - 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 RSsCommentaire 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/.
Softskills Non Hors domaine Non BeNeFri Oui Mobilité Oui UniPop Non -
Dates et salles
Date Heure Type d'enseignement Lieu 21.02.2022 09:15 - 12:00 Cours PER 21, salle E230 28.02.2022 09:15 - 12:00 Cours PER 21, salle E230 07.03.2022 09:15 - 12:00 Cours PER 21, salle E230 14.03.2022 09:15 - 12:00 Cours PER 21, salle E230 21.03.2022 09:15 - 12:00 Cours PER 21, salle E230 28.03.2022 09:15 - 12:00 Cours PER 21, salle E230 04.04.2022 09:15 - 12:00 Cours PER 21, salle E230 11.04.2022 09:15 - 12:00 Cours PER 21, salle E230 25.04.2022 09:15 - 12:00 Cours PER 21, salle E230 02.05.2022 09:15 - 12:00 Cours PER 21, salle E230 09.05.2022 09:15 - 12:00 Cours PER 21, salle E230 16.05.2022 09:15 - 12:00 Cours PER 21, salle E230 23.05.2022 09:15 - 12:00 Cours PER 21, salle E230 30.05.2022 09:15 - 12:00 Cours PER 21, salle E230 -
Modalités d'évaluation
Examen écrit
Mode d'évaluation Par note -
Affiliation
Valable pour les plans d'études suivants: BcMa - Data Analytics - 30 ECTS
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Complément au doctorat [PRE-DOC]
Version: 2020_1/v_01
Complément au doctorat ( Faculté des sciences et de médecine) > UE de spécialisation en Informatique (niveau master)
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Paquet indépendant des branches > UE de spécialisation en Informatique (niveau master)
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Formation continue > UE de spécialisation en Informatique (niveau master)
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Formation continue > UE de spécialisation en Informatique (niveau master)
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Version: 2010_2/V_02
MSc en informatique (BeNeFri), cours, séminaires et travail de Master > UE de spécialisation en Informatique (niveau master)
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Ma - Informatique de gestion - 90 ECTS
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