Introduction to recommender systems
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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, PER 21, Raum C230 (Frühlingssemester)
Strukturpläne 3h par semaine durant 14 semaines Kontaktstunden 42 Unterricht
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 RSsBemerkungen 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 -
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