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-2021

    Zeitplan und Räume

    Vorlesungszeiten Dienstag 14:15 - 17:00, Wöchentlich, PER 21, Raum E230
    Strukturpläne 3h par semaine durant 14 semaines
    Kontaktstunden 42

    Unterricht

    Verantwortliche
    Dozenten-innen
    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/.

    Soft Skills
    Nein
    ausserhalb des Bereichs
    Nein
    BeNeFri
    Ja
    Mobilität
    Ja
    UniPop
    Nein
  • Einzeltermine und Räume
    Datum Zeit Art der Unterrichtseinheit Ort
    23.02.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    02.03.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    09.03.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    16.03.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    23.03.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    30.03.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    13.04.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    20.04.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    27.04.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    04.05.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    11.05.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    18.05.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    25.05.2021 14:15 - 17:00 Kurs PER 21, Raum E230
    01.06.2021 14:15 - 17:00 Kurs PER 21, Raum E230
  • Leistungskontrolle

    Schriftliche Prüfung

    Bewertungsmodus Nach Note
  • Zuordnung
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    Zusatz zum Doktorat (Math.-Natw. Fakultät) [PRE-DOC]
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