extended-abstract

Workshop on Context-Aware Recommender Systems 2023

Authors:

Gediminas Adomavicius,

Konstantin Bauman,

Bamshad Mobasher,

Alexander Tuzhilin,

Moshe UngerAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 1234 - 1236

https://doi.org/10.1145/3604915.3608752

Published: 14 September 2023 Publication History

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 under-explored. The CARS 2023 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.

References

[1]

Gediminas Adomavicius, Linas Baltrunas, Ernesto William De Luca, Tim Hussein, and Alexander Tuzhilin. 2012. 4th workshop on context-aware recommender systems (CARS 2012). In 6th ACM Conference on Recommender Systems, RecSys 2012.

[2]

Gediminas Adomavicius, Linas Baltrunas, Tim Hussein, Francesco Ricci, and Alexander Tuzhilin. 2011. 3rd workshop on context-aware recommender systems (CARS 2011). In CEUR Workshop Proceedings, Vol. 791.

[3]

Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, and Moshe Unger. 2019. Workshop on context-aware recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems. 548–549.

[4]

Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, and Moshe Unger. 2020. Workshop on context-aware recommender systems. In Fourteenth ACM Conference on Recommender Systems. 635–637.

[5]

Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, and Moshe Unger. 2021. Workshop on Context-Aware Recommender Systems (CARS) 2021. In Fifteenth ACM Conference on Recommender Systems. 813–814.

[6]

Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, and Moshe Unger. 2022. CARS: Workshop on Context-Aware Recommender Systems 2022. In Proceedings of the 16th ACM Conference on Recommender Systems (Seattle, WA, USA) (RecSys ’22). Association for Computing Machinery, New York, NY, USA, 691–693. https://doi.org/10.1145/3523227.3547421

Digital Library

[7]

Gediminas Adomavicius, Konstantin Bauman, Alexander Tuzhilin, and Moshe Unger. 2022. Context-Aware Recommender Systems: From Foundations to Recent Developments. In Recommender Systems Handbook. Springer, 211–250.

[8]

Gediminas Adomavicius and Francesco Ricci. 2009. RecSys’ 09 workshop 3: workshop on context-aware recommender systems (CARS-2009). In Proceedings of the third ACM conference on Recommender systems. ACM, 423–424.

Digital Library

[9]

Gediminas Adomavicius and Alexander Tuzhilin. 2015. Context-aware recommender systems. In Recommender systems handbook. Springer, 191–226.

[10]

Gediminas Adomavicius, Alexander Tuzhilin, Shlomo Berkovsky, Ernesto W De Luca, and Alan Said. 2010. Context-aware recommender systems: research workshop and movie recommendation challenge. ACM RecSys2010 (2010), 26–30.

[11]

Konstantin Bauman and Alexander Tuzhilin. 2022. Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes. Information Systems Research 33, 1 (2022), 179–202.

Digital Library

[12]

Matthias Böhmer, Ernesto W De Luca, Alan Said, and Jaime Teevan. 2013. 3rd workshop on context-awareness in retrieval and recommendation. In Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 789–790.

[13]

Ernesto William De Luca, Matthias Böhmer, Alan Said, and Ed Chi. 2012. 2nd workshop on context-awareness in retrieval and recommendation:(carr 2012). In Proceedings of the 2012 ACM international conference on Intelligent User Interfaces. ACM, 409–412.

Digital Library

[14]

Ernesto William De Luca, Alan Said, Matthias Böhmer, and Florian Michahelles. 2011. Workshop on context-awareness in retrieval and recommendation. In Proceedings of the 16th international conference on Intelligent user interfaces. ACM, 471–472.

[15]

Ernesto William De Luca, Alan Said, Fabio Crestani, and David Elsweiler. 2015. 5th Workshop on Context-Awareness in Retrieval and Recommendation. In European Conference on Information Retrieval. Springer, 830–833.

[16]

Xichen Ding, Jie Tang, Tracy Liu, Cheng Xu, Yaping Zhang, Feng Shi, Qixia Jiang, and Dan Shen. 2019. Infer Implicit Contexts in Real-time Online-to-Offline Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2336–2346.

Digital Library

[17]

Negar Hariri, Bamshad Mobasher, and Robin Burke. 2012. Context-aware music recommendation based on latenttopic sequential patterns. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 131–138.

Digital Library

[18]

Xiaowen Huang, Shengsheng Qian, Quan Fang, Jitao Sang, and Changsheng Xu. 2018. Csan: Contextual self-attention network for user sequential recommendation. In Proceedings of the 26th ACM international conference on Multimedia. 447–455.

Digital Library

[19]

Dietmar Jannach and Michael Jugovac. 2019. Measuring the business value of recommender systems. ACM Transactions on Management Information Systems (TMIS) 10, 4 (2019), 1–23.

Digital Library

[20]

David Massimo and Francesco Ricci. 2021. Popularity, novelty and relevance in point of interest recommendation: an experimental analysis. J. Inf. Technol. Tour. 23, 4 (2021), 473–508.

[21]

Thuy Ngoc Nguyen, Francesco Ricci, Amra Delic, and Derek G. Bridge. 2019. Conflict resolution in group decision making: insights from a simulation study. User Model. User Adapt. Interact. 29, 5 (2019), 895–941.

[22]

Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-aware recommender systems. ACM Computing Surveys (CSUR) 51, 4 (2018), 1–36.

Digital Library

[23]

Lara Quijano-Sanchez, Juan A Recio-Garcia, and Belen Diaz-Agudo. 2010. Personality and social trust in group recommendations. In 2010 22Nd IEEE international conference on tools with artificial intelligence, Vol. 2. IEEE, 121–126.

Digital Library

[24]

Alan Said, Ernesto William De Luca, Daniele Quercia, and Matthias Böhmer. 2014. 4 th Workshop on Context-Awareness in Retrieval and Recommendation. In European Conference on Information Retrieval. Springer, 802–805.

[25]

Elena Smirnova and Flavian Vasile. 2017. Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks. arXiv preprint arXiv:1706.07684 (2017).

[26]

Moshe Unger, Ariel Bar, Bracha Shapira, and Lior Rokach. 2016. Towards latent context-aware recommendation systems. Knowledge-Based Systems 104 (2016), 165–178.

Digital Library

[27]

Moshe Unger, Pan Li, Maxime C Cohen, Brian Brost, and Alexander Tuzhilin. 2021. Deep multi-objective multi-stakeholder music recommendation. NYU Stern School of Business Forthcoming (2021).

[28]

Moshe Unger and Alexander Tuzhilin. 2020. Hierarchical latent context representation for context-aware recommendations. IEEE Transactions on Knowledge and Data Engineering (2020).

[29]

Moshe Unger, Alexander Tuzhilin, and Amit Livne. 2020. Context-Aware Recommendations Based on Deep Learning Frameworks. ACM Transactions on Management Information Systems (TMIS) 11, 2 (2020), 1–15.

Digital Library

[30]

Tianjun Wei and Tommy WS Chow. 2023. FGCR: Fused graph context-aware recommender system. Knowledge-Based Systems (2023), 110806.

[31]

Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, and Xing Xie. 2022. Graph convolution machine for context-aware recommender system. Frontiers of Computer Science 16, 6 (2022), 166614.

Digital Library

[32]

Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018. DRN: A deep reinforcement learning framework for news recommendation. In Proceedings of the 2018 World Wide Web Conference. 167–176.

Digital Library

Cited By

Aribas EDaglarli E(2024)Transforming Personalized Travel Recommendations: Integrating Generative AI with Personality ModelsElectronics10.3390/electronics1323475113:23(4751)Online publication date: 1-Dec-2024
https://doi.org/10.3390/electronics13234751
Adomavicius GBauman KMobasher BTuzhilin AUnger M(2024)Workshop on Context-Aware Recommender Systems (CARS) 2024Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687102(1219-1221)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687102

Index Terms

Workshop on Context-Aware Recommender Systems 2023
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 14 September 2023

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Extended-abstract
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Refereed limited

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RecSys '23

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Aribas EDaglarli E(2024)Transforming Personalized Travel Recommendations: Integrating Generative AI with Personality ModelsElectronics10.3390/electronics1323475113:23(4751)Online publication date: 1-Dec-2024
https://doi.org/10.3390/electronics13234751
Adomavicius GBauman KMobasher BTuzhilin AUnger M(2024)Workshop on Context-Aware Recommender Systems (CARS) 2024Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687102(1219-1221)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687102

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