skip to main content
10.1145/3640457.3687112acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
extended-abstract

The 1st International Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommendation (ROEGEN)

Published: 08 October 2024 Publication History

Abstract

We present an overview of a workshop focused on the exploration of generative models within recommender systems (RS). It highlights the dual nature of these technologies: on the one hand, they offer groundbreaking opportunities for enhancing RS through improved personalization, innovative content creation, and interactive user experiences; on the other hand, they introduce a range of challenges, including bias, misinformation, privacy concerns, and environmental impact.
The workshop, “ROEGen@RecSys. Risks, Opportunities, and Evaluations of Generative Models in Recommender Systems,” aims to address these issues by bringing together the research community to discuss strategies for understanding, evaluating, and mitigating the potential negative impacts of these technologies.
The workshop will cover three main themes: (i) the risks and challenges posed by the deployment of generative models in RS, the (ii) opportunities and applications of these technologies in enhancing RS, and (iii) strategies for the evaluation and mitigation of associated risks. Through this dialogue, the workshop intends to advance the field towards the development of generative models that are ethical, secure, environmentally sustainable, and beneficial for all stakeholders

References

[1]
Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, and Fedelucio Narducci. 2024. Interactive question answering systems: Literature review. Comput. Surveys 56, 9 (2024), 1–38.
[2]
Justin Cui, Kai Dicarlantonio, Sara Kemper, Kathy Lin, Danjie Tang, Anton Korikov, and Scott Sanner. 2024. Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-24). Washington D.C., USA.
[3]
Yashar Deldjoo. 2024. FairEvalLLM. A Comprehensive Framework for Benchmarking Fairness in Large Language Model Recommender Systems. arXiv preprint arXiv:2405.02219 (2024).
[4]
Yashar Deldjoo. 2024. Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency. arXiv preprint arXiv:2401.10545 (2024).
[5]
Yashar Deldjoo and Tommaso Di Noia. 2024. CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System. arXiv preprint arXiv:2403.05668 (2024).
[6]
Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, and Silvia Milano. 2024. A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys). KDD’24 (2024).
[7]
Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano, 2024. Recommendation with Generative Models. arXiv (2024).
[8]
Yashar Deldjoo, Fatemeh Nazary, Arnau Ramisa, Julian Mcauley, Giovanni Pellegrini, Alejandro Bellogin, and Tommaso Di Noia. 2023. A review of modern fashion recommender systems. Comput. Surveys 56, 4 (2023), 1–37.
[9]
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, 2022. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858 (2022).
[10]
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems. 299–315.
[11]
Chengkai Huang, Tong Yu, Kaige Xie, Shuai Zhang, Lina Yao, and Julian McAuley. 2024. Foundation Models for Recommender Systems: A Survey and New Perspectives. arXiv preprint arXiv:2402.11143 (2024).
[12]
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, 2024. Recommender systems with generative retrieval. Advances in Neural Information Processing Systems 36 (2024).
[13]
Matthias C. Rillig, Marlene Ågerstrand, Mohan Bi, Kenneth A. Gould, and Uli Sauerland. 2023. Risks and Benefits of Large Language Models for the Environment. Environmental Science & Technology 57, 9 (2023), 3464–3466. https://doi.org/10.1021/acs.est.3c01106
[14]
Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, and Lucas Dixon. 2023. Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences. In Proceedings of the 17th ACM Conference on Recommender Systems. Association for Computing Machinery, New York, NY, USA, 890–896. https://doi.org/10.1145/3604915.3608845
[15]
Tianshu Shen, Jiaru Li, Mohamed Reda Bouadjenek, Zheda Mai, and Scott Sanner. 2023. Towards understanding and mitigating unintended biases in language model-driven conversational recommendation. Inf. Process. Manage. 60, 1 (jan 2023), 21 pages. https://doi.org/10.1016/j.ipm.2022.103139
[16]
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023).
[17]
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, 2021. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021).
[18]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52, 1 (2019), 1–38.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
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.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2024

Check for updates

Qualifiers

  • Extended-abstract
  • Research
  • Refereed limited

Conference

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 91
    Total Downloads
  • Downloads (Last 12 months)91
  • Downloads (Last 6 weeks)19
Reflects downloads up to 18 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media