ORSUM 2022-5th Workshop on Online Recommender Systems and User Modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10448374" target="_blank" >RIV/00216208:11320/22:10448374 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3523227.3547411" target="_blank" >https://doi.org/10.1145/3523227.3547411</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3523227.3547411" target="_blank" >10.1145/3523227.3547411</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ORSUM 2022-5th Workshop on Online Recommender Systems and User Modeling

Popis výsledku v původním jazyce

Modern online systems for user modeling and recommendation need to continuously deal with complex data streams generated by users at very fast rates. This can be overwhelming for systems and algorithms designed to train recommendation models in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably for long periods of time. Online models that continuously learn from such flows of data are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online.The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy, fairness and transparency.

Název v anglickém jazyce

ORSUM 2022-5th Workshop on Online Recommender Systems and User Modeling

Popis výsledku anglicky

Modern online systems for user modeling and recommendation need to continuously deal with complex data streams generated by users at very fast rates. This can be overwhelming for systems and algorithms designed to train recommendation models in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably for long periods of time. Online models that continuously learn from such flows of data are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online.The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy, fairness and transparency.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

RecSys &apos;22: Proceedings of the 16th ACM Conference on Recommender Systems

ISBN

978-1-4503-9278-5

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

661-662

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Seattle WA, USA

Datum konání akce

18. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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