Introduction to recommender systems

  • Unterricht

    Details

    Fakultät Math.-Nat. und Med. Fakultät
    Bereich Informatik
    Code UE-SIN.08613
    Sprachen Englisch
    Art der Unterrichtseinheit Vorlesung
    Kursus Master
    Semester SP-2023

    Zeitplan und Räume

    Vorlesungszeiten Montag 09:15 - 12:00, Wöchentlich (Frühlingssemester)
    Strukturpläne 3h par semaine durant 14 semaines
    Kontaktstunden 42

    Unterricht

    Dozenten-innen
    • Teran Tamayo Luis Fernando
    Beschreibung

    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.

    Lernziele

    - 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 RSs

    Bemerkungen

    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/

    Soft Skills Nein
    ausserhalb des Bereichs Nein
    BeNeFri Ja
    Mobilität Ja
    UniPop Nein
  • Einzeltermine und Räume
    Datum Zeit Art der Unterrichtseinheit Ort
    20.02.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    27.02.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    06.03.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    13.03.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    20.03.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    27.03.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    03.04.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    17.04.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    24.04.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    01.05.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    08.05.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    15.05.2023 09:15 - 12:00 Kurs PER 21, Raum C230
    22.05.2023 09:15 - 12:00 Kurs PER 21, Raum C230
  • Leistungskontrolle

    Schriftliche Prüfung

    Bewertungsmodus Nach Note
  • Zuordnung
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