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The Effect of Feedback Granularity on Recommender Systems Performance

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10448371" target="_blank" >RIV/00216208:11320/22:10448371 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1145/3523227.3551479" target="_blank" >https://doi.org/10.1145/3523227.3551479</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3523227.3551479" target="_blank" >10.1145/3523227.3551479</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    The Effect of Feedback Granularity on Recommender Systems Performance

  • Original language description

    The main source of knowledge utilized in recommender systems (RS) is users&apos; feedback. While the usage of implicit feedback (i.e. user&apos;s behavior statistics) is gaining in prominence, the explicit feedback (i.e. user&apos;s ratings) remain an important data source. This is true especially for domains, where evaluation of an object does not require an extensive usage and users are well motivated to do so (e.g., video-on-demand services or library archives).So far, numerous rating schemes for explicit feedback have been proposed, ranging both in granularity and presentation style. There are several works studying the effect of rating&apos;s scale and presentation on user&apos;s rating behavior, e.g. willingness to provide feedback or various biases in rating behavior. Nonetheless, the effect of ratings granularity on RS performance remain largely under-researched.In this paper, we studied the combined effect of ratings granularity and supposed probability of feedback existence on various performance statistics of recommender systems. Results indicate that decreasing feedback granularity may lead to changes in RS&apos;s performance w.r.t. nDCG for some recommending algorithms. Nonetheless, in most cases the effect of feedback granularity is surpassed by even a small decrease in feedback&apos;s quantity. Therefore, our results corroborate the policy of many major real-world applications, i.e. preference of simpler rating schemes with the higher chance of feedback reception instead of finer-grained rating scenarios.

  • 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

    <a href="/en/project/GA22-21696S" target="_blank" >GA22-21696S: Deep Visual Representations of Unstructured Data</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    RecSys &apos;22: Proceedings of the 16th ACM Conference on Recommender Systems

  • ISBN

    978-1-4503-9278-5

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    586-591

  • Publisher name

    ACM

  • Place of publication

    New York, NY, USA

  • Event location

    Seattle WA, USA

  • Event date

    Sep 18, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article