Towards Results-level Proportionality for Multi-objective Recommender Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10447483" target="_blank" >RIV/00216208:11320/22:10447483 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3477495.3531787" target="_blank" >https://doi.org/10.1145/3477495.3531787</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3477495.3531787" target="_blank" >10.1145/3477495.3531787</a>
Alternative languages
Result language
angličtina
Original language name
Towards Results-level Proportionality for Multi-objective Recommender Systems
Original language description
The main focus of our work is the problem of multiple objectives optimization (MOO) while providing a final list of recommendations to the user. Currently, system designers can tune MOO by setting importance of individual objectives, usually in some kind of weighted average setting. However, this does not have to translate into the presence of such objectives in the final results. In contrast, in our work we would like to allow system designers or end-users to directly quantify the required relative ratios of individual objectives in the resulting recommendations, e.g., the final results should have 60% relevance, 30% diversity and 10% novelty. If individual objectives are transformed to represent quality on the same scale, these result conditioning expressions may greatly contribute towards recommendations tuneability and explainability as well as user's control over recommendations. To achieve this task, we propose an iterative algorithm inspired by the mandates allocation problem in public elections. The algorithm is applicable as long as per-item marginal gains of individual objectives can be calculated. Effectiveness of the algorithm is evaluated on several settings of relevance-novelty-diversity optimization problem. Furthermore, we also outline several options to scale individual objectives to represent similar value for the user.
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
SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
ISBN
978-1-4503-8732-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1963-1968
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Madrid, Spain
Event date
Jul 11, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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