Seminar hands-on recommender systems
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Unterricht
Details
Fakultät Math.-Nat. und Med. Fakultät Bereich Informatik Code UE-SIN.07827 Sprachen Englisch Art der Unterrichtseinheit Seminar
Kursus Master Semester SA-2022 Zeitplan und Räume
Strukturpläne 2h par semaine durant 14 semaines Kontaktstunden 28 Unterricht
Verantwortliche - Teran Tamayo Luis Fernando
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 (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.
Lernziele The objective of this seminar is that students will be able to develop a recommender system solution.
Bemerkungen 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/
Soft Skills Nein ausserhalb des Bereichs Nein BeNeFri Ja Mobilität Ja UniPop Nein -
Leistungskontrolle
Fortlaufende Evaluation
Bewertungsmodus Nach Note -
Zuordnung
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