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Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems

Published: 19 April 2023 Publication History

Abstract

Despite the potential impact of explanations on decision making, there is a lack of research on quantifying their effect on users’ choices. This paper presents an experimental protocol for measuring the degree to which positively or negatively biased explanations can lead to users choosing suboptimal recommendations. Key elements of this protocol include a preference elicitation stage to allow for personalizing recommendations, manual identification and extraction of item aspects from reviews, and a controlled method for introducing bias through the combination of both positive and negative aspects. We study explanations in two different textual formats: as a list of item aspects and as fluent natural language text. Through a user study with 129 participants, we demonstrate that explanations can significantly affect users’ selections and that these findings generalize across explanation formats.

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References

[1]
Krisztian Balog and Filip Radlinski. 2020. Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR ’20). 329–338.
[2]
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019. Transparent, Scrutable and Explainable User Models for Personalized Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR’19). 265–274.
[3]
Mustafa Bilgic and Raymond J. Mooney. 2005. Explaining Recommendations: Satisfaction vs. Promotion. In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces. 13–18.
[4]
Or Biran and Courtenay V. Cotton. 2017. Explanation and Justification in Machine Learning: A Survey. In IJCAI 2017 Workshop on Explainable Artificial Intelligence.
[5]
Shuo Chang, F. Maxwell Harper, and Loren Gilbert Terveen. 2016. Crowd-Based Personalized Natural Language Explanations for Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems(RecSys ’16). 175–182.
[6]
Li Chen, Guanliang Chen, and Feng Wang. 2015. Recommender Systems Based on User Reviews: The State of the Art. User Model. User-Adapt. Interact. 25, 2 (jun 2015), 99–154.
[7]
Li Chen and Feng Wang. 2014. Sentiment-Enhanced Explanation of Product Recommendations. In Proceedings of the 23rd International Conference on World Wide Web(WWW ’14). 239–240.
[8]
Li Chen and Feng Wang. 2017. Explaining Recommendations Based on Feature Sentiments in Product Reviews. In Proceedings of the 22nd International Conference on Intelligent User Interfaces(IUI ’17). 17–28.
[9]
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022. PaLM: Scaling Language Modeling with Pathways. arxiv:2204.02311 [cs.CL]
[10]
Felipe Costa, Sixun Ouyang, Peter Dolog, and Aonghus Lawlor. 2018. Automatic Generation of Natural Language Explanations. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion(IUI ’18).
[11]
Fatih Gedikli, Dietmar Jannach, and Mouzhi Ge. 2014. How Should I Explain? A Comparison of Different Explanation Types for Recommender Systems. Int. J. Hum. Comput. Stud. 72, 4 (2014), 367–382.
[12]
Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy, and Gerhard Weikum. 2021. ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models. In Proceedings of the Web Conference 2021(WWW ’21). 3850–3860.
[13]
Leo A. Goodman. 1965. On Simultaneous Confidence Intervals for Multinomial Proportions. Technometrics 7, 2 (may 1965), 247–254.
[14]
Jason L. Harman, John O’Donovan, Tarek Abdelzaher, and Cleotilde Gonzalez. 2014. Dynamics of Human Trust in Recommender Systems. In Proceedings of the 8th ACM Conference on Recommender Systems(RecSys ’14). 305–308.
[15]
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.
[16]
Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. TriRank: Review-Aware Explainable Recommendation by Modeling Aspects. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management(CIKM ’15). 1661–1670.
[17]
Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. 2000. Explaining Collaborative Filtering Recommendations. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work(CSCW ’00). 241–250.
[18]
María Hernández-Rubio, Iván Cantador, and Alejandro Bellogín. 2019. A Comparative Analysis of Recommender Systems Based on Item Aspect Opinions Extracted from User Reviews. User Model. User-Adapt. Interact. 29 (apr 2019), 381–441.
[19]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining(ICDM ’08). 263–272.
[20]
Johannes Kunkel, Tim Donkers, Lisa Michael, Catalin-Mihai Barbu, and Jürgen Ziegler. 2019. Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems(CHI ’19). 1–12.
[21]
Khalil Ibrahim Muhammad, Aonghus Lawlor, and Barry Smyth. 2016. A Live-User Study of Opinionated Explanations for Recommender Systems. In Proceedings of the 21st International Conference on Intelligent User Interfaces(IUI ’16). 256–260.
[22]
Cataldo Musto, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2019. Justifying Recommendations through Aspect-Based Sentiment Analysis of Users Reviews. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization(UMAP ’19). 4–12.
[23]
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 188–197.
[24]
Xia Ning and George Karypis. 2011. SLIM: Sparse Linear Methods for Top-N Recommender Systems. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining(ICDM ’11). 497–506.
[25]
Ingrid Nunes and Dietmar Jannach. 2017. A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems. User Model. User-adapt. Interact. 27, 3-5 (2017), 393–444.
[26]
Georgina Peake and Jun Wang. 2018. Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(KDD ’18). 2060–2069.
[27]
Gustavo Penha, Eyal Krikon, and Vanessa Murdock. 2022. Pairwise Review-Based Explanations for Voice Product Search. In ACM SIGIR Conference on Human Information Interaction and Retrieval(CHIIR ’22). 300–304.
[28]
Alessandro Piscopo, Oana Inel, Sanne Vrijenhoek, Martijn Millecamp, and Krisztian Balog. 2022. Report on the 1st Workshop on Measuring the Quality of Explanations in Recommender Systems (QUARE 2022) at SIGIR 2022. SIGIR Forum 56, 2 (dec 2022).
[29]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based Collaborative Filtering Recommendation Algorithms. In Proceedings of the 10th International Conference on World Wide Web(WWW ’01). 285–295.
[30]
Nava Tintarev and Judith Masthoff. 2012. Evaluating the Effectiveness of Explanations for Recommender Systems. User Model. User-Adapt. Interact. 22 (oct 2012), 399–439.
[31]
Nava Tintarev and Judith Masthoff. 2015. Explaining Recommendations: Design and Evaluation. In Recommender Systems Handbook (2nd ed.), Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). Springer US, Chapter 10, 353–382.
[32]
Jesse Vig, Shilad Sen, and John Riedl. 2009. Tagsplanations: Explaining Recommendations Using Tags. In Proceedings of the 14th International Conference on Intelligent User Interfaces(IUI ’09). 47–56.
[33]
Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, and Ilya Eckstein. 2021. DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume(EACL ’21). 2540–2549.
[34]
Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Found. Trends Inf. Retr. 14, 1 (2020), 1–101.

Cited By

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  • (2024)The Explanation That Hits Home: The Characteristics of Verbal Explanations That Affect Human Perception in Subjective Decision-MakingProceedings of the ACM on Human-Computer Interaction10.1145/36870568:CSCW2(1-37)Online publication date: 8-Nov-2024
  • (2024)The Personalization of Justified Recommendations Using the Users Profile Interest and ReviewsIntelligent Informatics10.1007/978-981-97-2147-4_12(159-175)Online publication date: 18-Oct-2024

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cover image ACM Conferences
CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
April 2023
3914 pages
ISBN:9781450394222
DOI:10.1145/3544549
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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Published: 19 April 2023

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Author Tags

  1. Explainable recommendation
  2. evaluating explanations
  3. explanation bias
  4. explanation types
  5. natural language justifications

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View all
  • (2024)The Explanation That Hits Home: The Characteristics of Verbal Explanations That Affect Human Perception in Subjective Decision-MakingProceedings of the ACM on Human-Computer Interaction10.1145/36870568:CSCW2(1-37)Online publication date: 8-Nov-2024
  • (2024)The Personalization of Justified Recommendations Using the Users Profile Interest and ReviewsIntelligent Informatics10.1007/978-981-97-2147-4_12(159-175)Online publication date: 18-Oct-2024

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