Introduction to recommender systems

  • Teaching

    Details

    Faculty Faculty of Science and Medicine
    Domain Computer Science
    Code UE-SIN.08613
    Languages English
    Type of lesson Lecture
    Level Master
    Semester SP-2021

    Schedules and rooms

    Summary schedule Tuesday 14:15 - 17:00, Hebdomadaire, PER 21, Room E230
    Struct. of the schedule 3h par semaine durant 14 semaines
    Contact's hours 42

    Teaching

    Responsibles
    Teachers
    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 (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.

    Training objectives

    - 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

    Comments

    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/.

    Softskills
    No
    Off field
    No
    BeNeFri
    Yes
    Mobility
    Yes
    UniPop
    No
  • Dates and rooms
    Date Hour Type of lesson Place
    23.02.2021 14:15 - 17:00 Cours PER 21, Room E230
    02.03.2021 14:15 - 17:00 Cours PER 21, Room E230
    09.03.2021 14:15 - 17:00 Cours PER 21, Room E230
    16.03.2021 14:15 - 17:00 Cours PER 21, Room E230
    23.03.2021 14:15 - 17:00 Cours PER 21, Room E230
    30.03.2021 14:15 - 17:00 Cours PER 21, Room E230
    13.04.2021 14:15 - 17:00 Cours PER 21, Room E230
    20.04.2021 14:15 - 17:00 Cours PER 21, Room E230
    27.04.2021 14:15 - 17:00 Cours PER 21, Room E230
    04.05.2021 14:15 - 17:00 Cours PER 21, Room E230
    11.05.2021 14:15 - 17:00 Cours PER 21, Room E230
    18.05.2021 14:15 - 17:00 Cours PER 21, Room E230
    25.05.2021 14:15 - 17:00 Cours PER 21, Room E230
    01.06.2021 14:15 - 17:00 Cours PER 21, Room E230
  • Assessments methods

    Examen écrit

    Assessments methods By rating
  • Assignment
    Valid for the following curricula:
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    Version: ens_compl_sciences
    Paquet indépendant des branches > Specialized courses in Computer Science (Master level)

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    Business Communication - Information Systems 90 ECTS [MA]
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    Courses - 60 ECTS > Option Group > Information Management > Cours > Module Informatik > Data Science

    Computer Science [3e cycle]
    Version: 2015_1/V_01
    Supplement to doctoral studies in Computer Science > Specialized courses in Computer Science (Master level)

    Data Analytics 30 [MA]
    Version: 2020/SA-v01
    À choix 9 crédits ECTS > Data Science

    Information Management 90 ECTS [MA] - SA/2019
    Version: 2019/SA_V01
    Classes - min. 45 ECTS > Module IT and IT Management > Data Science

    Information Management 90 ECTS [MA] - SA/2020
    Version: 2020/SA-v01
    Classes - min. 45 ECTS > Module IT and IT Management > Data Science

    MSc in Computer science (BeNeFri)
    Version: 2010_2/V_02
    MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > Specialized courses in Computer Science (Master level)