Probabilistic Modeling, Learnability and Uncertainty Estimation for Interaction Prediction in Movie Rating Datasets

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00387677" target="_blank" >RIV/68407700:21240/25:00387677 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3705328.3759332" target="_blank" >https://doi.org/10.1145/3705328.3759332</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3705328.3759332" target="_blank" >10.1145/3705328.3759332</a>

Result language

angličtina

Original language name

Probabilistic Modeling, Learnability and Uncertainty Estimation for Interaction Prediction in Movie Rating Datasets

Original language description

In this paper, we examine the hypothesis that the interactions recorded in many Recommendation Systems datasets are distributed according to a low-rank distribution, i.e. a mixture of factorizable distributions. Surprisingly, we find that on several popular datasets, a simple non-negative matrix factorization method equals or outperforms more modern methods such as LightGCN, which indicates that the sampling distribution over interactions is indeed low-rank. Furthermore, we mathematically prove that low-rank distributions are learnable with a sparse number of observations (where m/n and r refer to the number of users/items and the non-negative rank respectively) both in terms of the total variation norm and in terms of the expected recall at k, arguably providing some of the first generalization bounds for recommender systems in the implicit feedback setting. We also provide a modified version of the NMF algorithm which provides further performance improvements compared to the standard NMF baseline on the smaller datasets considered. Finally, we propose the theoretically grounded concept of empirical expected recall as an uncertainty estimate for probabilistic models of the recommendation task, and demonstrate its success in a setting where user-wise abstentions are allowed.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Article name in the collection

RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems

ISBN

979-8-4007-1364-4

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

1261-1266

Publisher name

ACM

Place of publication

New York

Event location

Prague

Event date

Sep 22, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001572100200183