Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks
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Explanations did not improve trust calibration because explanations were perceived as information overload by human decision-makers.
“Indeed, several studies discussed reasons and situations where explanations did not improve trust calibration, e.g., explanations were perceived as an information overload by the humans’ decision-makers [55].”
From Explainable recommendation: when design meets trust calibration (2021) · cites this paper as [55]
The Information overload section says explanations can overwhelm end-users and become confusing. The Under-trust section says more explanation detail may not increase trust, but the paper does not establish that perceived information overload caused a failure to improve trust calibration. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823 · see citing works
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations
Andreou, A., Venkatadri, G., Goga, O., Gummadi, K. P., Loiseau, P., & Mislove, A. E. (2018) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23191 · Cited by 1,505 · see citing works
Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works
Manipulating and Measuring Model Interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728 · see citing works
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0279-x · Cited by 709 · see citing works
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · see citing works
Principles of Explanatory Debugging to Personalize Interactive Machine Learning
Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701399 · Cited by 576 · see citing works
The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems
Bussone, A., Stumpf, S., & O'Sullivan, D. (2015) · 2015 International Conference on Healthcare Informatics · doi:10.1109/ichi.2015.26 · Cited by 441 · see citing works
A systematic review and taxonomy of explanations in decision support and recommender systems
Nunes, I., & Jannach, D. (2017) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-017-9195-0 · Cited by 416 · see citing works
Too much, too little, or just right? Ways explanations impact end users' mental models
Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I., & Wong, W.-K. (2013) · 2013 IEEE Symposium on Visual Languages and Human Centric Computing · doi:10.1109/vlhcc.2013.6645235 · Cited by 388 · see citing works
Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377 · see citing works
Toward Human-Understandable, Explainable AI
Hagras, H. (2018) · Computer · doi:10.1109/mc.2018.3620965 · Cited by 350 · see citing works
How should I explain? A comparison of different explanation types for recommender systems
Gedikli, F., Jannach, D., & Ge, M. (2014) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2013.12.007 · Cited by 337 · see citing works
Explaining models: an empirical study of how explanations impact fairness judgment
Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., & Dugan, C. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302310 · Cited by 286 · see citing works
Bostandjiev, S., O’Donovan, J., & Höllerer, T. (2012) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2365952.2365964 · Cited by 274 · see citing works
Assessing demand for intelligibility in context-aware applications
Lim, B. Y., & Dey, A. K. (2009) · Proceedings of the 11th international conference on Ubiquitous computing · doi:10.1145/1620545.1620576 · Cited by 246 · see citing works
Explainability in human–agent systems
Rosenfeld, A., & Richardson, A. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09408-y · Cited by 228 · see citing works
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Kulesza, T., Stumpf, S., Burnett, M., & Kwan, I. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207678 · Cited by 225 · see citing works
The effects of example-based explanations in a machine learning interface
Cai, C. J., Jongejan, J., & Holbrook, J. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302289 · Cited by 220 · see citing works
Interacting meaningfully with machine learning systems: Three experiments
Stumpf, S., Rajaram, V., Li, L., Wong, W.-K., Burnett, M., Dietterich, T., Sullivan, E., & Herlocker, J. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.03.004 · Cited by 184 · see citing works
Communicating Algorithmic Process in Online Behavioral Advertising
Eslami, M., Krishna Kumaran, S. R., Sandvig, C., & Karahalios, K. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174006 · Cited by 171 · see citing works
Visualizing recommendations to support exploration, transparency and controllability
Verbert, K., Parra, D., Brusilovsky, P. L., et al. (2013) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2449396.2449442 · Cited by 163 · see citing works
Explainable Agency for Intelligent Autonomous Systems
Langley, P., Meadows, B., Sridharan, M., & Choi, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i2.19108 · Cited by 156 · see citing works
Narayanan, M., Chen, E., He, J., Kim, B., Gershman, S., & Doshi-Velez, F. (2018) · arXiv · doi:10.48550/arxiv.1802.00682 · Cited by 153 · see citing works
The Impact of Placebic Explanations on Trust in Intelligent Systems
Eiband, M., Buschek, D., Kremer, A., & Hussmann, H. (2019) · Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290607.3312787 · Cited by 152 · see citing works
Millecamp, M., Htun, N. N., Conati, C., & Verbert, K. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302313 · Cited by 148 · see citing works
Towards Health (Aware) Recommender Systems
Schäfer, H., Hors-Fraile, S., Karumur, R. P., Valdez, A. S. C., Said, A., Torkamaan, H., Ulmer, T., & Trattner, C. (2017) · International Conference on Digital Health · doi:10.1145/3079452.3079499 · Cited by 139 · see citing works
Making Decisions about Privacy
Knijnenburg, B. P., & Kobsa, A. (2013) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2499670 · Cited by 136 · see citing works
EU regulations on algorithmic decision-making and a "right to explanation".Preprint
Goodman, B., & Flaxman, S. (2016) · arXiv (Cornell University) · Cited by 134 · see citing works
I can do better than your AI: expertise and explanations
Schaffer, J., O'Donovan, J., Michaelis, J., Raglin, A., & Höllerer, T. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302308 · Cited by 119 · see citing works
Springer, A., & Whittaker, S. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302322 · Cited by 115 · cited 6× · see citing works
“On-demand method is useful where explanations contain a high level of information so users may get distracted and need more time to consume it [27, 76].”
BackgroundInfluentialSmallWorlds: Visualizing Social Recommendations
Gretarsson, B., O’Donovan, J., Bostandjiev, S., et al. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01679.x · Cited by 112 · see citing works
Are explanations always important?: a study of deployed, low-cost intelligent interactive systems
Bunt, A., Lount, M., & Lauzon, C. (2012) · Proceedings of the 2012 ACM international conference on Intelligent User Interfaces · doi:10.1145/2166966.2166996 · Cited by 106 · see citing works
Crowdsourcing: A taxonomy and systematic mapping study
Hosseini, M., Shahri, A., Phalp, K. T., Taylor, J., & Ali, R. (2015) · Computer Science Review · doi:10.1016/j.cosrev.2015.05.001 · Cited by 94 · see citing works
Self-citationEstablishing Appropriate Trust via Critical States
Huang, S. H., Bhatia, K., Abbeel, P., & Dragan, A. D. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593649 · Cited by 94 · see citing works
Parra, D., Brusilovsky, P. L., & Trattner, C. (2014) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2557500.2557542 · Cited by 88 · see citing works
Human-Centric Justification of Machine Learning Predictions
Biran, O., & McKeown, K. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/202 · Cited by 84 · see citing works
FeatureInsight: Visual support for error-driven feature ideation in text classification
Brooks, M., Amershi, S., Lee, B., Drucker, S. M., Kapoor, A., & Simard, P. (2015) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2015.7347637 · Cited by 80 · see citing works
Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations
Tamagnini, P., Krause, J., Dasgupta, A., & Bertini, E. (2017) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/3077257.3077260 · Cited by 70 · see citing works
What do online behavioral advertising privacy disclosures communicate to users?
Leon, P. G., Cranshaw, J., Cranor, L. F., Graves, J., Hastak, M., Ur, B., & Xu, G. (2012) · ACM Workshop on Privacy in the Electronic Society (WPES) · doi:10.1145/2381966.2381970 · Cited by 62 · see citing works
When people and algorithms meet: user-reported problems in intelligent everyday applications
Eiband, M., Völkel, S. T., Buschek, D., Cook, S., & Hussmann, H. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302262 · Cited by 62 · see citing works
Towards an ethical recommendation framework
Paraschakis, D. (2017) · International Conference on Research Challenges in Information Science (RCIS) · doi:10.1109/rcis.2017.7956539 · Cited by 60 · see citing works
Explaining recommendations in an interactive hybrid social recommender
Tsai, C.-H., & Brusilovsky, P. L. (2019) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3301275.3302318 · Cited by 60 · see citing works
The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images
Dominguez, V., Messina, P., Donoso-Guzmán, I., & Parra, D. (2019) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3301275.3302274 · Cited by 53 · see citing works
Loepp, B., Herrmanny, K., & Ziegler, J. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702496 · Cited by 51 · see citing works
Personalized Reason Generation for Explainable Song Recommendation
Zhao, G., Fu, H., Song, R., Sakai, T., Chen, Z., Xie, X., & Qian, X. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3337967 · Cited by 48 · see citing works
Providing explanations for recommendations in reciprocal environments
Kleinerman, A., Rosenfeld, A., & Kraus, S. (2018) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3240323.3240362 · Cited by 45 · see citing works
Taking advice from intelligent systems: the double-edged sword of explanations
Ehrlich, K., Kirk, S. E., Patterson, J., Rasmussen, J. C., Ross, S. I., & Gruen, D. (2011) · International Conference on Intelligent User Interfaces · doi:10.1145/1943403.1943424 · Cited by 42 · see citing works
Dark Patterns of Explainability, Transparency, and User Control for Intelligent Systems.
Chromik, M., Eiband, M., Völkel, S. T., & Buschek, D. (2019) · IUI Workshops · Cited by 42 · see citing works
Personalising Explainable Recommendations: Literature and Conceptualisation
Naiseh, M., Jiang, N., Ma, J., & Ali, R. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-45691-7_49 · Cited by 42 · see citing works
Self-citationKnowledgeable Explanations for Recommender Systems
Zanker, M., & Ninaus, D. (2010) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2010.131 · Cited by 40 · see citing works
Schaffer, J., Giridhar, P., Jones, D. G., Höllerer, T., Abdelzaher, T., & O’Donovan, J. (2015) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2678025.2701406 · Cited by 39 · see citing works
Great Explanations: Opinionated Explanations for Recommendations
Muhammad, K., Lawlor, A., Rafter, R., & Smyth, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24586-7_17 · Cited by 39 · see citing works
Explaining and exploring job recommendations
Gutiérrez, F., Charleer, S., Croon, R. D., et al. (2019) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3298689.3347001 · Cited by 39 · see citing works
A Chat-Based Group Recommender System for Tourism
Nguyen, T. N., & Ricci⋆, F. (2017) · Springer eBooks · doi:10.1007/978-3-319-51168-9_2 · Cited by 34 · see citing works
Providing Control and Transparency in a Social Recommender System for Academic Conferences
Tsai, C.-H., & Brusilovsky, P. L. (2017) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3079628.3079701 · Cited by 32 · see citing works
Toit, E. R. D. (2012) · Perspectives in Education · doi:10.38140/pie.v30i2.1757 · Cited by 28 · see citing works
Improving User Confidence in Concept Maps
Bras, P. L., Robb, D. A., Methven, T. S., Padilla, S., & Chantler, M. J. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173978 · Cited by 26 · see citing works
Explaining Recommendations Using Contexts
Sato, M., Ahsan, B., Nagatani, K., Sonoda, T., Zhang, Q., & Ohkuma, T. (2018) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3172944.3173012 · Cited by 24 · see citing works
Explaining Need-based Educational Recommendations Using Interactive Open Learner Models
Barria-Pineda, J., Akhüseyinoğlu, K., & Brusilovsky, P. L. (2019) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3314183.3323463 · Cited by 21 · see citing works
Do News Consumers Want Explanations for Personalized News Rankings?
ter Hoeve, M., Heruer, M., Odijk, D., Schuth, A., Spitters, M., & de Rijke, M. (2018) · doi:10.18122/b24d7n · Cited by 20 · see citing works
Visualization of Explanations in Recommender Systems
Al-Taie, M. Z., & Kadry, S. (2014) · Journal of Advanced Management Science · doi:10.12720/joams.2.2.140-144 · Cited by 19 · see citing works
Interaction Design in a Mobile Food Recommender System
Elahi, M., Ge, M., Ricci⋆, F., et al. (2015) · Hispana · Cited by 18 · see citing works
Designing an Explanation Interface for Proactive Recommendations in Automotive Scenarios
Bader, R., Woerndl, W., Karitnig, A., & Leitner, G. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28509-7_10 · Cited by 16 · see citing works
Explanatory dialogues with argumentative faculties over inconsistent knowledge bases
Arioua, A., Buche, P., & Madalina Croitoru, A. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.009 · Cited by 16 · see citing works
Κάργα, Σ., & Satratzemi, M. (2019) · Education and Information Technologies · doi:10.1007/s10639-019-09909-z · Cited by 16 · see citing works
Towards understandable personalized recommendations: Hybrid explanations
Švrček, M., Kompan, M., & Bieliková, M. (2018) · Computer Science and Information Systems · doi:10.2298/csis171217012s · Cited by 13 · see citing works
Improving Children’s Experience on a Mobile EdTech Platform through a Recommender System
Ruiz‐Iniesta, A., Melgar, L., Baldominos, A., & Quintana, D. (2018) · Mobile Information Systems · doi:10.1155/2018/1374017 · Cited by 13 · see citing works
Editorial for EAIT issue 1, 2019
Tatnall, A. (2019) · Education and Information Technologies · doi:10.1007/s10639-018-09856-1 · Cited by 13 · see citing works
Streamlining patient consultations for sleep disorders with a knowledge-based CDSS
Blake, J., Kerr, D. V., & Gammack, J. G. (2015) · Information Systems · doi:10.1016/j.is.2015.08.003 · Cited by 12 · see citing works
An empirical study on the persuasiveness of fact-based explanations for recommender systems
Zanker, M., & Schoberegger, M. (2014) · Cited by 12 · see citing works
Explanation of spreading activation based recommendations
Hussein, T., & Neuhaus, S. (2010) · doi:10.1145/2002375.2002381 · Cited by 11 · see citing works
Wiebe, M., Geiskkovitch, D. Y., & Bunt, A. (2016) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2856767.2856814 · Cited by 11 · see citing works
FATREC Workshop on Responsible Recommendation Proceedings
Ekstrand, M. D., & Sharma, A. K. (2017) · Scholar Works (Boise State University) · Cited by 11 · see citing works
The effect of explanations on perceived control and behaviors in intelligent systems
Holliday, D., Wilson, S. M., & Stumpf, S. (2013) · CHI Extended Abstracts · doi:10.1145/2468356.2468389 · Cited by 10 · see citing works
What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs
Kang, B., Tintarev, N., Höllerer, T., & O’Donovan, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47874-6_20 · Cited by 10 · see citing works
Using user emotions to trigger UI adaptation
Galindo-Losada, J., Dupuy-Chessa, S. S., Mandran, N., & Céret, É. (2018) · International Conference on Research Challenges in Information Science (RCIS) · doi:10.1109/rcis.2018.8406661 · Cited by 10 · see citing works
Li, T., Convertino, G., Tayi, R. K., et al. (2019) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3301275.3302294 · Cited by 10 · see citing works
An interactive interface for instilling trust and providing diverse recommendations
Katarya, R., Jain, I., & Hasija, H. (2014) · International Conference on Computer and Communication Technology · doi:10.1109/iccct.2014.7001463 · Cited by 9 · see citing works
Normative vs. Pragmatic: Two Perspectives on the Design of Explanations in Intelligent Systems.
Eiband, M., Schneider, H., & Buschek, D. (2018) · IUI Workshops · Cited by 9 · see citing works
Explaining smart heating systems to discourage fiddling with optimized behavior
Stumpf, S., Skrebe, S., Aymer, G., & Hobson, J. (2018) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · Cited by 6 · see citing works
Exploring Users' Perception of Collaborative Explanation Styles
Çoba, L., Zanker, M., Rook, L., et al. (2018) · IEEE Conference on Business Informatics · doi:10.1109/cbi.2018.00017 · Cited by 6 · see citing works
What Do Online Behavioral Advertising Disclosures Communicate to Users? (CMU-CyLab-12-008)
Leon, P. G., Cranshaw, J., Cranor, L. F., et al. (2018) · Figshare · doi:10.1184/r1/6468134 · Cited by 5 · see citing works
Hu, J., Zhang, Z., Liu, J., et al. (2016) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2959100.2959112 · Cited by 4 · see citing works
A TV Program Discovery Dialog System using recommendations
Ramachandran, D., Fanty, M., Provine, R., et al. (2015) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w15-4659 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Hu, J., Zhang, Z., Liu, J., Shi, C., Yu, P.S., Wang, B.: Recexp: A semantic recom- mender system with explanation based on heterogeneous information network. In: Proceedings of the 10th ACM Conference on Recommender Systems. pp. 401-402. ACM (2016)
- Krause, J., Perer, A., Bertini, E.: A user study on the effect of aggregating ex- planations for interpreting machine learning models. In: ACM KDD Workshop on Interactive Data Exploration and Analytics (2018)
- Kroll, J.A., Barocas, S., Felten, E.W., Reidenberg, J.R., Robinson, D.G., Yu, H.: Accountable algorithms. U. Pa. L. Rev. 165, 633 (2016)
- Lamche, B., Adıgüzel, U., Wörndl, W.: Interactive explanations in mobile shop- ping recommender systems. In: Joint Workshop on Interfaces and Human Decision Making in Recommender Systems. p. 14 (2014)
- Lepri, B., Oliver, N., Letouzé, E., Pentland, A., Vinck, P.: Fair, transparent, and ac- countable algorithmic decision-making processes. Philosophy & Technology 31(4), 611-627 (2018)
Cited by
Sorted by citations
Explainable recommendation: when design meets trust calibration
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2021) · World Wide Web · doi:10.1007/s11280-021-00916-0 · Cited by 68 · see citing works
Self-citation
- Authors
- Naiseh, M., Jiang, N., Ma, J., & Ali, R.
- Affiliations
- Bournemouth University (GB)
- Chengdu University of Information Technology (CN)
- Hamad bin Khalifa University (QA)
- Corresponding author
- Naiseh, Mohammad
- Venue
- Lecture Notes in Business Information Processing
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 212-228
- ISSN
- 1865-1348 (ISSN-L)
- 1865-1356
- OpenAlex
- W3037100559
- Cited by
- 27
- References
- 88
- Citations by year
- 2026: 4 · 2024: 7 · 2023: 4 · 2022: 4 · 2021: 7 · 2020: 1
- FWCI
- 1.53
- Type
- conference paper
- Language
- en
- Open access
- Locations
- Lecture notes in business information processing (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Bournemouth University Research Online (Bournemouth University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- explainable recommendation, user trust, user experience, decision-making, transparency, human-AI interaction
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