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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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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)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Article name in the collection

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

ISBN

978-1-4503-9891-6

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

296-301

Publisher name

ACM

Place of publication

New York, NY, USA

Event location

Limassol, Cyprus

Event date

Jun 26, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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