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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

Proceedings of the 17th ACM Conference on Recommender Systems

ISBN

979-8-4007-0241-9

ISSN

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e-ISSN

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Number of pages

2

Pages from-to

1272-1273

Publisher name

ACM

Place of publication

New York, NY, USA

Event location

Singapore, Singapore

Event date

Sep 18, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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