The Effect of Similarity Metric and Group Size on Outlier Selection & Satisfaction in Group Recommender Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10468874" target="_blank" >RIV/00216208:11320/23:10468874 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3563359.3597386" target="_blank" >https://doi.org/10.1145/3563359.3597386</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3563359.3597386" target="_blank" >10.1145/3563359.3597386</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Effect of Similarity Metric and Group Size on Outlier Selection & Satisfaction in Group Recommender Systems

Popis výsledku v původním jazyce

Group recommender systems (GRS) are a specific case of recommender systems (RS), where recommendations are constructed to a group of users rather than an individual. GRS has diverse application areas including trip planning, recommending movies to watch together, or music in shared environments. However, due to the lack of large datasets with group decision-making feedback information, or even the group definitions, GRS approaches are often evaluated offline w.r.t. individual user feedback and artificially generated groups. These synthetic groups are usually constructed w.r.t. pre-defined group size and inter-user similarity metric. While numerous variants of synthetic group generation procedures were utilized so far, its impact on the evaluation results was not sufficiently discussed. In this paper, we address this research gap by investigating the impact of various synthetic group generation procedures, namely the usage of different user similarity metrics and the effect of group sizes. We consider them in the context of &quot;outlier vs. majority&quot; groups, where a group of similar users is extended with one or more diverse ones. Experimental results indicate a strong impact of the selected similarity metric on both the typical characteristics of selected outliers as well as the performance of individual GRS algorithms. Moreover, we show that certain algorithms better adapt to larger groups than others.

Název v anglickém jazyce

The Effect of Similarity Metric and Group Size on Outlier Selection & Satisfaction in Group Recommender Systems

Popis výsledku anglicky

Group recommender systems (GRS) are a specific case of recommender systems (RS), where recommendations are constructed to a group of users rather than an individual. GRS has diverse application areas including trip planning, recommending movies to watch together, or music in shared environments. However, due to the lack of large datasets with group decision-making feedback information, or even the group definitions, GRS approaches are often evaluated offline w.r.t. individual user feedback and artificially generated groups. These synthetic groups are usually constructed w.r.t. pre-defined group size and inter-user similarity metric. While numerous variants of synthetic group generation procedures were utilized so far, its impact on the evaluation results was not sufficiently discussed. In this paper, we address this research gap by investigating the impact of various synthetic group generation procedures, namely the usage of different user similarity metrics and the effect of group sizes. We consider them in the context of &quot;outlier vs. majority&quot; groups, where a group of similar users is extended with one or more diverse ones. Experimental results indicate a strong impact of the selected similarity metric on both the typical characteristics of selected outliers as well as the performance of individual GRS algorithms. Moreover, we show that certain algorithms better adapt to larger groups than others.

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)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

ISBN

978-1-4503-9891-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

296-301

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Limassol, Cyprus

Datum konání akce

26. 6. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—