Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial Perspective

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00368607" target="_blank" >RIV/68407700:21240/23:00368607 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3565472.3595630" target="_blank" >https://doi.org/10.1145/3565472.3595630</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3565472.3595630" target="_blank" >10.1145/3565472.3595630</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial Perspective

Popis výsledku v původním jazyce

The popularity of linear shallow autoencoders for collaborative filtering is growing in the research community, and internet industry providers of Recommender Systems are also taking notice. However, despite their simplicity and accuracy, these models often cannot be used in real-world industrial recommender systems due to their inability to scale to very large interaction matrices. Our research aims to address this issue by developing a scalable, explainable, and accurate shallow linear autoencoder method for collaborative filtering that meets the demands of real-world recommenders. In this paper, we present our industrial Ph.D. research project, which includes: (1) the development of a scalable method called ELSA and the adaptation of the method to a large real-world recommender and (2) the creation of a framework to visualize the recommender systems insights based on modeling the distribution of retrieval metrics in latent user space. We discuss the current status of our project, the key steps to finish the project, and the possible future extensions after the dissertation.

Název v anglickém jazyce

Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial Perspective

Popis výsledku anglicky

The popularity of linear shallow autoencoders for collaborative filtering is growing in the research community, and internet industry providers of Recommender Systems are also taking notice. However, despite their simplicity and accuracy, these models often cannot be used in real-world industrial recommender systems due to their inability to scale to very large interaction matrices. Our research aims to address this issue by developing a scalable, explainable, and accurate shallow linear autoencoder method for collaborative filtering that meets the demands of real-world recommenders. In this paper, we present our industrial Ph.D. research project, which includes: (1) the development of a scalable method called ELSA and the adaptation of the method to a large real-world recommender and (2) the creation of a framework to visualize the recommender systems insights based on modeling the distribution of retrieval metrics in latent user space. We discuss the current status of our project, the key steps to finish the project, and the possible future extensions after the dissertation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

ISBN

978-1-4503-9932-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

290-295

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Limassol

Datum konání akce

26. 6. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001051715400031