Uncertainty-Adjusted Recommendation via Matrix Factorization With Weighted Losses

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00382222" target="_blank" >RIV/68407700:21240/24:00382222 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/TNNLS.2023.3288769" target="_blank" >https://doi.org/10.1109/TNNLS.2023.3288769</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TNNLS.2023.3288769" target="_blank" >10.1109/TNNLS.2023.3288769</a>

Result language

angličtina

Original language name

Uncertainty-Adjusted Recommendation via Matrix Factorization With Weighted Losses

Original language description

In a recommender systems (RSs) dataset, observed ratings are subject to unequal amounts of noise. Some users might be consistently more conscientious in choosing the ratings they provide for the content they consume. Some items may be very divisive and elicit highly noisy reviews. In this article, we perform a nuclear-norm-based matrix factorization method which relies on side information in the form of an estimate of the uncertainty of each rating. A rating with a higher uncertainty is considered more likely to be erroneous or subject to large amounts of noise, and therefore more likely to mislead the model. Our uncertainty estimate is used as a weighting factor in the loss we optimize. To maintain the favorable scaling and theoretical guarantees coming with nuclear norm regularization even in this weighted context, we introduce an adjusted version of the trace norm regularizer which takes the weights into account. This regularization strategy is inspired from the weighted trace norm which was introduced to tackle nonuniform sampling regimes in matrix completion. Our method exhibits state-of-the-art performance on both synthetic and real life datasets in terms of various performance measures, confirming that we have successfully used the auxiliary information extracted.

Czech name

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Czech description

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Type

J_imp - 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)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

IEEE Transactions on Neural Networks and Learning Systems

ISSN

2162-237X

e-ISSN

2162-2388

Volume of the periodical

35

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

15624-15637

UT code for WoS article

001030658300001

EID of the result in the Scopus database

2-s2.0-85164745356