All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

An Adaptive Filter for Preference Fine-Tuning in Recommender Systems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00133947" target="_blank" >RIV/00216224:14330/23:00133947 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-031-24197-0_7" target="_blank" >http://dx.doi.org/10.1007/978-3-031-24197-0_7</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-24197-0_7" target="_blank" >10.1007/978-3-031-24197-0_7</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    An Adaptive Filter for Preference Fine-Tuning in Recommender Systems

  • Original language description

    A recommender system may recommend certain items that the users would not prefer. This can be caused by either the imperfection of the recommender system or the change of user preferences. When those failed recommendations appear often in the system, the users may consider that the recommender system is not able to capture the user preference. This can result in abandoning to further use the recommender system. However, given the possible failed recommendations, most recommender systems will ignore the non-preferred recommendations. Therefore, this paper proposes failure recovery solution for recommender systems with an adaptive filter. On the one hand, the proposed solution can deal with the failed recommendations while keeping the user engagement. Additionally, it allows the recommender system to dynamically fine tune the preferred items and become a long-term application. Also, the adaptive filter can avoid the cost of constantly updating the recommender learning model.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2020, WEBIST 2021

  • ISBN

    9783031241963

  • ISSN

    1865-1348

  • e-ISSN

  • Number of pages

    15

  • Pages from-to

    107-121

  • Publisher name

    SPRINGER INTERNATIONAL PUBLISHING AG

  • Place of publication

    CHAM

  • Event location

    CHAM

  • Event date

    Jan 1, 2023

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article

    000972038800007