Workshop on Context-Aware Recommender Systems

Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Alexander Tuzhilin; Moshe Unger

doi:10.1145/3705328.3747978

Workshop on Context-Aware Recommender Systems

Gediminas Adomavicius
, Konstantin Bauman
, Bamshad Mobasher
, Alexander Tuzhilin
, Moshe Unger

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain underexplored. The CARS 2025 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

Original language	English
Title of host publication	RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems
Publisher	Association for Computing Machinery, Inc
Pages	1409-1411
Number of pages	3
ISBN (Electronic)	9798400713644
DOIs	https://doi.org/10.1145/3705328.3747978
State	Published - 7 Aug 2025
Externally published	Yes
Event	19th ACM Conference on Recommender Systems, RecSys 2025 - Prague, Czech Republic Duration: 22 Sep 2025 → 26 Sep 2025

Publication series

Name	RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems

Conference

Conference	19th ACM Conference on Recommender Systems, RecSys 2025
Country/Territory	Czech Republic
City	Prague
Period	22/09/25 → 26/09/25

Keywords

Context
Context-Aware Recommendation
Contextual Modeling
Sequence-Aware Recommendation

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Software
Control and Systems Engineering

Access to Document

10.1145/3705328.3747978

Cite this

Adomavicius, G., Bauman, K., Mobasher, B., Tuzhilin, A., & Unger, M. (2025). Workshop on Context-Aware Recommender Systems. In RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems (pp. 1409-1411). (RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/3705328.3747978

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title = "Workshop on Context-Aware Recommender Systems",

abstract = "Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain underexplored. The CARS 2025 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.",

keywords = "Context, Context-Aware Recommendation, Contextual Modeling, Sequence-Aware Recommendation",

author = "Gediminas Adomavicius and Konstantin Bauman and Bamshad Mobasher and Alexander Tuzhilin and Moshe Unger",

note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 19th ACM Conference on Recommender Systems, RecSys 2025 ; Conference date: 22-09-2025 Through 26-09-2025",

year = "2025",

month = aug,

day = "7",

doi = "10.1145/3705328.3747978",

language = "English",

series = "RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery, Inc",

pages = "1409--1411",

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}

Adomavicius, G, Bauman, K, Mobasher, B, Tuzhilin, A & Unger, M 2025, Workshop on Context-Aware Recommender Systems. in RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems. RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, pp. 1409-1411, 19th ACM Conference on Recommender Systems, RecSys 2025, Prague, Czech Republic, 22/09/25. https://doi.org/10.1145/3705328.3747978

Workshop on Context-Aware Recommender Systems. / Adomavicius, Gediminas; Bauman, Konstantin; Mobasher, Bamshad et al.
RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc, 2025. p. 1409-1411 (RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bauman, Konstantin

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AU - Tuzhilin, Alexander

AU - Unger, Moshe

PY - 2025/8/7

Y1 - 2025/8/7

N2 - Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain underexplored. The CARS 2025 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

AB - Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain underexplored. The CARS 2025 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

KW - Context

KW - Context-Aware Recommendation

KW - Contextual Modeling

KW - Sequence-Aware Recommendation

UR - https://www.scopus.com/pages/publications/105019643891

U2 - 10.1145/3705328.3747978

DO - 10.1145/3705328.3747978

M3 - Conference contribution

AN - SCOPUS:105019643891

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SP - 1409

EP - 1411

BT - RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems

PB - Association for Computing Machinery, Inc

T2 - 19th ACM Conference on Recommender Systems, RecSys 2025

Y2 - 22 September 2025 through 26 September 2025

ER -

Workshop on Context-Aware Recommender Systems

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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