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Are We Losing Interest in Context-Aware Recommender Systems?

Published: 28 June 2024 Publication History

Abstract

Contextual information is a prerequisite for timely offering of personalized decision support and recommendation. Yet, research on context-aware recommender systems (CARS) does not appear to be thriving, and finding public datasets containing context factors is a challenging task. We can make various assumptions about why this drop in research interest happened – be it ethical considerations or the popularity of opaque deep learning models that merely consider context in an implicit way. This is an unwelcome development. We argue that continued effort must be put on the creation of suitable datasets. Furthermore, we see significant opportunities in the development of next-generation CARS in the space of interactive AI assistants powered by Large Language Models.

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References

[1]
Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (2005), 734–749.
[2]
Gediminas Adomavicius and Alexander Tuzhilin. 2010. Context-aware recommender systems. In Recommender Systems Handbook. Springer, 217–253.
[3]
Monya Baker. 2016. 1,500 scientists lift the lid on reproducibility. Nature 533, 7604 (2016).
[4]
Robin D. Burke. 1999. The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence. 844–849.
[5]
Emanuele Cavenaghi, Gabriele Sottocornola, Fabio Stella, and Markus Zanker. 2023. A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation Systems. ACM Transactions on Recommender Systems 1, 3 (2023), 1–23.
[6]
Alexander Felfernig, Gerhard Friedrich, Dietmar Jannach, and Markus Zanker. 2006. An Integrated Environment for the Development of Knowledge-Based Recommender Applications. Int. J. Electron. Commer. 11, 2 (2006), 11–34.
[7]
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems. 101–109.
[8]
Sergio Ilarri, Raquel Trillo-Lado, and Ramón Hermoso. 2018. Datasets for context-aware recommender systems: Current context and possible directions. In 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). 25–28.
[9]
Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2022. A Survey on Conversational Recommender Systems. ACM Comput. Surv. 54, 5 (2022), 105:1–105:36.
[10]
Dietmar Jannach, Pearl Pu, Francesco Ricci, and Markus Zanker. 2022. Recommender systems: Trends and frontiers. AI Magazine 43, 2 (2022), 145–150.
[11]
Dietmar Jannach, Paul Resnick, Alexander Tuzhilin, and Markus Zanker. 2016. Recommender systems—beyond matrix completion. Commun. ACM 59, 11 (2016), 94–102.
[12]
Dietmar Jannach, Markus Zanker, Mouzhi Ge, and Marian Gröning. 2012. Recommender systems in computer science and information systems–a landscape of research. In 13th International Conference on E-Commerce and Web Technologies, EC-Web 2012. 76–87.
[13]
Saurabh Kulkarni and Sunil F Rodd. 2020. Context Aware Recommendation Systems: A review of the state of the art techniques. Computer Science Review 37 (2020), 100255.
[14]
Dhananjay Nayakankuppam and Joseph R Priester. 2014. Putting context effects in context: the construction and retrieval as moderated by attitude strength (CARMAS) model of evaluative judgment. In Handbook of Brand Relationships. Routledge, 327–348.
[15]
Clint LP Pennings, Jan van Dalen, and Laurens Rook. 2019. Coordinating judgmental forecasting: Coping with intentional biases. Omega 87 (2019), 46–56.
[16]
Shaina Raza and Chen Ding. 2019. Progress in context-aware recommender systems—An overview. Computer Science Review 31 (2019), 84–97.
[17]
Laurens Rook, Adem Sabic, and Markus Zanker. 2020. Engagement in proactive recommendations: The role of recommendation accuracy, information privacy concerns and personality traits. Journal of Intelligent Information Systems 54, 1 (2020), 79–100.
[18]
Saul Vargas, Pablo Castells, and David Vallet. 2011. Intent-oriented diversity in recommender systems. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1211–1212.
[19]
Paul Voigt and Axel Von dem Bussche. 2017. The EU general data protection regulation (GDPR). A Practical Guide, 1st Ed., Cham: Springer International Publishing 10, 3152676 (2017), 10–5555.
[20]
Markus Zanker and Markus Jessenitschnig. 2009. Case-studies on exploiting explicit customer requirements in recommender systems. User Model. User Adapt. Interact. 19, 1-2 (2009), 133–166.
[21]
Markus Zanker, Laurens Rook, and Dietmar Jannach. 2019. Measuring the impact of online personalisation: Past, present and future. International Journal of Human-Computer Studies 131 (2019), 160–168.
[22]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. Comput. Surveys 52, 1 (2019), 1–38.

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  • (2024)Second Workshop on Context Representation in User ModellingAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3658528(225-228)Online publication date: 27-Jun-2024

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    cover image ACM Conferences
    UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
    June 2024
    662 pages
    ISBN:9798400704666
    DOI:10.1145/3631700
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    New York, NY, United States

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    Published: 28 June 2024

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    Author Tags

    1. Context
    2. Context-awareness
    3. Personalization
    4. Recommender Systems
    5. User Intent

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    • (2024)Second Workshop on Context Representation in User ModellingAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3658528(225-228)Online publication date: 27-Jun-2024

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