Sahil Verma
Chirag Shah
ABSTRACT
Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models underlying these recommender systems are often enormously large and black-box in nature for users, content providers, and system developers alike. It is often crucial for all stakeholders to understand the model's rationale behind making certain predictions and recommendations. This is especially true for the content providers whose livelihoods depend on the recommender system. Drawing motivation from the practitioners' need, in this work, we propose a recourse framework for recommender systems, targeted towards the content providers. Algorithmic recourse in the recommendation setting is a set of actions that, if executed, would modify the recommendations (or ranking) of an item in the desired manner. A recourse suggests actions of the form: ''if a feature changes X to Y, then the ranking of that item for a set of users will change to X.'' Furthermore, we demonstrate that RecRec is highly effective in generating valid, sparse, and actionable recourses through an empirical evaluation of recommender systems trained on three real-world datasets. To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.
- Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Recommender Systems as Multistakeholder Environments. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (Bratislava, Slovakia) (UMAP '17). Association for Computing Machinery, New York, NY, USA, 2 pages. https: //doi.org/10.1145/3079628.3079657Google Scholar
Digital Library
- 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 (jun 2005), 734--749.Google ScholarDigital Library
- Charu C Aggarwal. 2016. Content-based recommender systems. In Recommender systems. Springer, 139--166.Google Scholar
- Thomas Blumensath and Mike E. Davies. 2009. Iterative hard thresholding for compressed sensing. Applied and Computational Harmonic Analysis 27, 3 (2009), 265--274. https://www.sciencedirect.com/science/article/pii/S1063520309000384Google ScholarCross Ref
- Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender systems survey. Knowledge-based systems 46 (2013), 109-- 132.Google ScholarDigital Library
- Eliane Léontine Bucher, Peter Kalum Schou, and Matthias Waldkirch. 2020. Pacifying the algorithm - Anticipatory compliance in the face of algorithmic management in the gig economy. Organization 28 (2020), 44 -- 67.Google ScholarCross Ref
- Laurent Candillier, Kris Jack, Françoise Fessant, and Frank Meyer. 2009. State-ofthe- art recommender systems. In Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling. IGI Global, 1--22.Google Scholar
- Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. 2015. "I Always Assumed That I Wasn't Really That Close to [Her]": Reasoning about Invisible Algorithms in News Feeds. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI '15). Association for Computing Machinery, New York, NY, USA, 10 pages. https://doi.org/10.1145/2702123.2702556Google ScholarDigital Library
- F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (Dec. 2015), 19 pages. https://doi.org/10.1145/2827872Google ScholarDigital Library
- Maya Holikatti, Shagun Jhaver, and Neha Kumar. 2019. Learning to Airbnb by Engaging in Online Communities of Practice. Proc. ACM Hum.-Comput. Interact. 3, CSCW (2019), 19 pages. https://doi.org/10.1145/3359330Google ScholarDigital Library
- Mohammad Hossein Jarrahi and Will Sutherland. 2019. Algorithmic Management and Algorithmic Competencies: Understanding and Appropriating Algorithms in Gig Work. In Information in Contemporary Society. Springer International Publishing, Cham, 578--589.Google Scholar
- Shagun Jhaver, Yoni Karpfen, and Judd Antin. 2018. Algorithmic Anxiety and Coping Strategies of Airbnb Hosts. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI '18). Association for Computing Machinery, New York, NY, USA, 12 pages. https://doi.org/10.1145/3173574.3173995Google ScholarDigital Library
- Min Kyung Lee. 2018. Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society 5 (2018).Google Scholar
- Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011. Contentbased recommender systems: State of the art and trends. Recommender systems handbook (2011), 73--105.Google Scholar
- Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267 (2019). https://doi.org/10.1016/ j.artint.2018.07.007Google Scholar
- Hatim A. Rahman. 2021. The Invisible Cage: Workers' Reactivity to Opaque Algorithmic Evaluations. Administrative Science Quarterly 66, 4 (2021), 945--988. https://doi.org/10.1177/00018392211010118Google ScholarCross Ref
- Lubna Razaq, Beth Kolko, and Gary Hsieh. 2022. Making crafting visible while rendering labor invisible on the Etsy platform. In Designing Interactive Systems Conference 2021 (Virtual Event, USA) (DIS '22). Association for Computing Machinery, New York, NY, USA, 15 pages.Google Scholar
- Kunal Shah, Akshaykumar Salunke, Saurabh Dongare, and Kisandas Antala. 2017. Recommender systems: An overviewof different approaches to recommendations. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 1--4.Google ScholarCross Ref
- Guy Shani and Asela Gunawardana. 2011. Evaluating Recommendation Systems. Springer US, Boston, MA, 257--297. https://doi.org/10.1007/978-0--387--85820--3_8Google Scholar
- Özge Sürer, Robin Burke, and Edward C. Malthouse. 2018. Multistakeholder Recommendation with Provider Constraints. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys '18). Association for Computing Machinery, New York, NY, USA, 9 pages. https://doi.org/10.1145/3240323.3240350Google ScholarDigital Library
- Poonam B Thorat, RM Goudar, and Sunita Barve. 2015. Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications 110, 4 (2015), 31--36.Google ScholarCross Ref
- Tianchi. 2018. Ad Display/Click Data on Taobao.com. https://tianchi.aliyun.com/ dataset/dataDetail?dataId=56Google Scholar
- Sahil Verma, John Dickerson, and Keegan Hines. 2020. Counterfactual Explanations for Machine Learning: A Review. arXiv:2010.10596 [cs.LG]Google Scholar
- Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2018. Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. arXiv:1711.00399 [cs.AI]Google Scholar
- Mengting Wan and Julian J. McAuley. 2018. Item recommendation on monotonic behavior chains. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2--7, 2018. ACM, 86--94. https://doi.org/10.1145/3240323.3240369Google ScholarDigital Library
- Mengting Wan, Rishabh Misra, Ndapa Nakashole, and Julian J. McAuley. 2019. Fine-Grained Spoiler Detection from Large-Scale Review Corpora. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers. Association for Computational Linguistics, 2605--2610. https://doi.org/10.18653/v1/p19--1248Google Scholar
- Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Found. Trends Inf. Retr. 14 (2020), 1--101.Google ScholarDigital Library
- Yong Zheng. 2019. Multi-Stakeholder Recommendations: Case Studies, Methods and Challenges (RecSys '19). Association for Computing Machinery, New York, NY, USA, 2 pages. https://doi.org/10.1145/3298689.3346951Google ScholarDigital Library
Index Terms
- RecRec: Algorithmic Recourse for Recommender Systems
Recommendations
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied ComputingOver the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...
Comments