ORSUM 2023-6th 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%2F23%3A10469916" target="_blank" >RIV/00216208:11320/23:10469916 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3604915.3608763" target="_blank" >https://doi.org/10.1145/3604915.3608763</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3604915.3608763" target="_blank" >10.1145/3604915.3608763</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ORSUM 2023-6th Workshop on Online Recommender Systems and User Modeling

Popis výsledku v původním jazyce

Modern online platforms for user modeling and recommendation require complex data infrastructures to collect and process data. Some of this data has to be kept to later be used in batches to train personalization models. However, since user activity data can be generated at very fast rates it is also useful to have algorithms able to process data streams online, in real time. Given the continuous and potentially fast change of content, context and user preferences or intents, stream-based models, and their synchronization with batch models can be extremely challenging. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably over long periods of time. Models able to continuously learn from such flows of data are gaining attention in the recommender systems community, and are being increasingly deployed in online platforms. However, many challenges associated with learning from streams need further investigation.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 reproducibility, privacy, fairness, diversity, transparency, auditability, and compliance with recently adopted or upcoming legal frameworks worldwide.

Název v anglickém jazyce

ORSUM 2023-6th Workshop on Online Recommender Systems and User Modeling

Popis výsledku anglicky

Modern online platforms for user modeling and recommendation require complex data infrastructures to collect and process data. Some of this data has to be kept to later be used in batches to train personalization models. However, since user activity data can be generated at very fast rates it is also useful to have algorithms able to process data streams online, in real time. Given the continuous and potentially fast change of content, context and user preferences or intents, stream-based models, and their synchronization with batch models can be extremely challenging. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably over long periods of time. Models able to continuously learn from such flows of data are gaining attention in the recommender systems community, and are being increasingly deployed in online platforms. However, many challenges associated with learning from streams need further investigation.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 reproducibility, privacy, fairness, diversity, transparency, auditability, and compliance with recently adopted or upcoming legal frameworks worldwide.

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 17th ACM Conference on Recommender Systems

ISBN

979-8-4007-0241-9

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

1272-1273

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Singapore, Singapore

Datum konání akce

18. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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