Personalized Implicit Negative Feedback Enhancements for Fuzzy D'Hondt's Recommendation Aggregations

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10422758" target="_blank" >RIV/00216208:11320/20:10422758 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3428757.3429105" target="_blank" >https://doi.org/10.1145/3428757.3429105</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3428757.3429105" target="_blank" >10.1145/3428757.3429105</a>

Result language
angličtina
Original language name
Personalized Implicit Negative Feedback Enhancements for Fuzzy D'Hondt's Recommendation Aggregations
Original language description
In this paper, we focus on the problems of fair aggregation of recommender systems (RS) and over-exposure of users with insignificant recommendations. While fair aggregation of diverse RS may contribute to both calibration and diversity challenges, some recently proposed methods suffer from repeating the same set of recommendations to the user over and over again. However, it may be difficult to distinguish between situations when users ignore recommendations because they are irrelevant or because they did not notice them. In order to cope with these challenges, we propose an innovative off-line RS evaluation methodology based on the noticeability of recommended items. We further propose a Fuzzy D'Hondt's algorithm with personalized implicit negative feedback attribution (FDHondtINF). The algorithm is designed to provide a fair ordering of candidate items coming from multiple individual RS, while considering also the objects previously ignored by the current user. FDHondtINF was evaluated off-line along with other aggregation methods and individual RS on MovieLens 1M dataset. The algorithm performs especially well in situations when the recommended items are less noticeable, or when a sequence of multiple recommendations for the same user model is given.
Czech name
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Czech description
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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)

Project
<a href="/en/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexible models for known-item search in large video collections</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
iiWAS '20: Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services
ISBN
978-1-4503-8922-8
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
210-215
Publisher name
ACM
Place of publication
New York, USA
Event location
Chiang Mai Thailand
Event date
Nov 30, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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