The Effect of Feedback Granularity on Recommender Systems Performance
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
The Effect of Feedback Granularity on Recommender Systems Performance
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
The Effect of Feedback Granularity on Recommender Systems Performance
Popis výsledku anglicky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/GA22-21696S" target="_blank" >GA22-21696S: Hluboké vizuální reprezentace nestrukturovaných dat</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
ISBN
978-1-4503-9278-5
ISSN
—
e-ISSN
—
Počet stran výsledku
6
Strana od-do
586-591
Název nakladatele
ACM
Místo vydání
New York, NY, USA
Místo konání akce
Seattle WA, USA
Datum konání akce
18. 9. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—