Uncertainty-adjusted Inductive Matrix Completion with Graph Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00368260" target="_blank" >RIV/68407700:21240/23:00368260 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3604915.3610654" target="_blank" >https://doi.org/10.1145/3604915.3610654</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3604915.3610654" target="_blank" >10.1145/3604915.3610654</a>

Result language

angličtina

Original language name

Uncertainty-adjusted Inductive Matrix Completion with Graph Neural Networks

Original language description

We propose a robust recommender systems model which performs matrix completion and a ratings-wise uncertainty estimation jointly. Whilst the prediction module is purely based on an implicit low-rank assumption imposed via nuclear norm regularization, our loss function is augmented by an uncertainty estimation module which learns an anomaly score for each individual rating via a Graph Neural Network: data points deemed more anomalous by the GNN are downregulated in the loss function used to train the low-rank module. The whole model is trained in an end-to-end fashion, allowing the anomaly detection module to tap on the supervised information available in the form of ratings. Thus, our model’s predictors enjoy the favourable generalization properties that come with being chosen from small function space (i.e., low-rank matrices), whilst exhibiting the robustness to outliers and flexibility that comes with deep learning methods. Furthermore, the anomaly scores themselves contain valuable qualitative information. Experiments on various real-life datasets demonstrate that our model outperforms standard matrix completion and other baselines, confirming the usefulness of the anomaly detection module.

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

—

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

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

ISBN

979-8-4007-0241-9

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

1169-1174

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Singapur

Event date

Sep 18, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001156630300147