Rank-sensitive proportional aggregations in dynamic recommendation scenarios
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10448380" target="_blank" >RIV/00216208:11320/22:10448380 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=RPXhiLPyJ5" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=RPXhiLPyJ5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11257-021-09311-w" target="_blank" >10.1007/s11257-021-09311-w</a>
Alternative languages
Result language
angličtina
Original language name
Rank-sensitive proportional aggregations in dynamic recommendation scenarios
Original language description
In this paper, we focus on the problem of rank-sensitive proportionality preservation when aggregating outputs of multiple recommender systems in dynamic recommendation scenarios. We believe that individual recommenders may provide complementary views on the user's preferences or needs, and therefore, their proportional (i.e. unbiased) aggregation may be beneficial for the long-term user satisfaction. We propose an aggregation framework (FuzzDA) based on a modified D'Hondt's algorithm (DA) for proportional mandates allocation. Specifically, we adjusted DA to register fuzzy membership of items and modified the selection procedure to balance both relevance and proportionality criteria. Furthermore, we propose several iterative votes assignment strategies and negative implicit feedback incorporation strategies to make FuzzDA framework applicable in dynamic recommendation scenarios. Overall, the framework should provide benefits w.r.t. long-term novelty of recommendations, diversity of recommended items as well as overall relevance. We evaluated FuzzDA framework thoroughly both in offline simulations and in online A/B testing. Framework variants outperformed baselines w.r.t. click-through rate (CTR) in most of the evaluated scenarios. Some variants of FuzzDA also provided the best or close-to-best iterative novelty (while maintaining very high CTR). While the impact of the framework variants on user-wise diversity was not so extensive, the trade-off between CTR and diversity seems reasonable.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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/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)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
User Modelling and User-Adapted Interaction
ISSN
0924-1868
e-ISSN
1573-1391
Volume of the periodical
32
Issue of the periodical within the volume
4
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
62
Pages from-to
685-746
UT code for WoS article
000736936900001
EID of the result in the Scopus database
2-s2.0-85122018051