Seminar hands-on recommender systems
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Enseignement
Détails
Faculté Faculté des sciences et de médecine Domaine Informatique Code UE-SIN.07827 Langues Anglais Type d'enseignement Séminaire
Cursus Master Semestre(s) SA-2022 Horaires et salles
Struct. des horaires 2h par semaine durant 14 semaines Heures de contact 28 Enseignement
Responsables - Teran Tamayo Luis Fernando
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 (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.
Objectifs de formation The objective of this seminar is that students will be able to develop a recommender system solution.
Commentaire 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 Non Hors domaine Non BeNeFri Oui Mobilité Oui UniPop Non -
Modalités d'évaluation
Evaluation continue
Mode d'évaluation Par note -
Affiliation
Valable pour les plans d'études suivants: 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)
Enseignement complémentaire en sciences
Version: ens_compl_sciences
Paquet indépendant des branches > UE de spécialisation en Informatique (niveau master)
Informatique [3e cycle]
Version: 2015_1/V_01
Formation continue > UE de spécialisation en Informatique (niveau master)
Informatique [POST-DOC]
Version: 2015_1/V_01
Formation continue > UE de spécialisation en Informatique (niveau master)
MSc en informatique (BeNeFri)
Version: 2023_1/V_01
MSc en informatique (BeNeFri), cours, séminaires et travail de Master > T5 : Information Systems and Decision SupportMSc en informatique (BeNeFri), cours, séminaires et travail de Master > T6: Data Science