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' feedback. While the usage of implicit feedback (i.e. user's behavior statistics) is gaining in prominence, the explicit feedback (i.e. user'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's scale and presentation on user'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'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'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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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 '22: Proceedings of the 16th ACM Conference on Recommender Systems
ISBN
978-1-4503-9278-5
ISSN
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e-ISSN
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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
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