ABSTRACT
What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions.
- Amershi, S., Fogarty, J., Kapoor, A., and Tan, D. Examining multiple potential models in end-user interactive concept learning. In Proc. CHI, ACM (2010), 1357--1360. Google ScholarDigital Library
- Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 8, 2 (1977).Google Scholar
- Benedek, J. and Miner, T. Measuring desirability: New methods for evaluating desirability in a usability lab setting. In Proc. Usability Professionals Association International Conference (2002).Google Scholar
- Bozionelos, N. The relationship of instrumental and expressive traits with computer anxiety. Personality and Individual Differences 31 (2001), 955--974.Google ScholarCross Ref
- Compeau, D. and Higgins, C. Application of social cognitive theory to training for computer skills. Information Systems Research, 6,2 (1995), 118--143.Google ScholarDigital Library
- Echo Nest, The. http://the.echonest.com (July, 2011).Google Scholar
- Fiebrink, R., Cook, P., and Trueman, D. Human model evaluation in interactive supervised learning. In Proc. CHI, ACM (2011), 147--156. Google ScholarDigital Library
- Hart, S. and Staveland, L. Development of a NASA-TLX (Task load index): Results of empirical and theoretical research, Hancock, P. and Meshkati, N. (Eds.), Human Mental Workload (1988), 139--183.Google Scholar
- Herlocker, J., Konstan, J., Riedl, J. Explaining collaborative filtering recommendations. In Proc. CSCW, ACM (2000), 241--250. Google ScholarDigital Library
- Johnson-Laird, P.N. Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge University Press (1983). Google ScholarDigital Library
- Kapoor, A., Lee, B., Tan, D., and Horvitz, E. Interactive optimization for steering machine classification. In Proc. CHI, ACM (2010), 1343--1352. Google ScholarDigital Library
- Kolb, D. A. Experiential Learning. Prentice-Hall Englewood Cliffs, NJ (1984).Google Scholar
- Kulesza, T., Wong, W.-K., Stumpf, S., Perona, S., White, R., Burnett, M., Oberst, I., and Ko, A. J. Fixing the program my computer learned: barriers for end users, barriers for the machine. In Proc. IUI, ACM (2009), 187--196. Google ScholarDigital Library
- Kulesza, T., Stumpf, S., Burnett, M., Wong, W., Riche, Y., Moore, T., Oberst, I., Shinsel, A., McIntosh, K. Explanatory debugging: Supporting end-user debugging of machine-learned programs. In Proc. VL/HCC, IEEE (2010), 41--48. Google ScholarDigital Library
- Lim, B. Y., Dey, A. K., and Avrahami, D. Why and why not explanations improve the intelligibility of contextaware intelligent systems. Proc. CHI, ACM (2009), 2119- 2128. Google ScholarDigital Library
- Lim, B. Y. and Dey, A. K. Toolkit to support intelligibility in context-aware applications. In Proc. UbiComp, ACM (2010), 13--22. Google ScholarDigital Library
- McNee, S. M., Lam, S. K., Guetzlaff, C., Konstan, J. A., and Riedl, J. Confidence displays and training in recommender systems. In Proc. INTERACT, IFIP (2003), 176--183.Google Scholar
- Norman, D. Some observations on mental models, Gentner, D. and Stevens, A. (Eds.), Mental Models (1983), 7--14.Google Scholar
- Pandora Media, Inc. Initial Public Offering Form S-1 (2011).Google Scholar
- Rosson, M. B., Carroll, J. M., and Bellamy, R. K. E. Smalltalk scaffolding: a case study of minimalist instruction. In Proc. CHI, ACM (1990), 423--430. Google ScholarDigital Library
- Sharp, H., Rogers, Y., and Preece, J. Interaction Design: Beyond Human-Computer Interaction (3rd edition), John Wiley (2011). Google ScholarDigital Library
- Sinha, R. R. and Swearingen, K. The role of transparency in recommender systems. In Proc. CHI Extended Abstracts, ACM (2002), 830--831. Google ScholarDigital Library
- Stone, P., Dunphy, D., Smith, M., Ogilvie, D., and associates. The General Inquirer: A Computer Approach to Content Analysis. The MIT Press (1966).Google Scholar
- Stumpf, S., Rajaram, V., Li, L., Burnett, M., Dietterich, T., Sullivan, E., Drummond, R., and Herlocker, J. Toward harnessing user feedback for machine learning. In Proc. IUI, ACM (2007), 82--91. Google ScholarDigital Library
- Stumpf, S., Rajaram, V., Li, L., Burnett, M., Wong, W.K., Dietterich, T., Sullivan, E., Drummond, R., and Herlocker, J. Interacting meaningfully with machine learning systems: Three experiments. International Journal of Human-Computer Studies 67, 8 (2009). Google ScholarDigital Library
- Talbot, J., Lee, B., Tan, D., and Kapoor, A. EnsembleMatrix: Interactive visualization to support machine learning with multiple classifiers. In Proc. CHI, ACM (2009), 1283--1292. Google ScholarDigital Library
- Tintarev, N., and Masthoff, J. Effective explanations of recommendations: User-centered design. In Proc. Recommender Systems (2007), 153--156. Google ScholarDigital Library
- Tullio, J., Dey, A.K., Chalecki, J., and Fogarty, J. How it works: A field study of non-technical users interacting with an intelligent system. In Proc CHI, ACM (2007). Google ScholarDigital Library
- Wilfong, J. Computer anxiety and anger: The impact of computer use, computer experience, and self-efficacy beliefs. Computers in Human Behavior 22 (2006).Google Scholar
Index Terms
- Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Recommendations
User Profiling for Web Page Filtering
To help address pressing problems with information overload, researchers have developed personal agents to provide assistance to users in navigating the Web. To provide suggestions, such agents rely on user profiles representing interests and ...
My Dating Site Thinks I'm a Loser: effects of personal photos and presentation intervals on perceptions of recommender systems
CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsReceiving poor results from a personalized recommendation system is frustrating. When users try to compensate by putting on a "different face" and game the system, the results can be even more frustrating. This paper investigates how to improve the user ...
How Much Novelty is Relevant?: It Depends on Your Curiosity
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information RetrievalTraditional recommendation systems (RS's) aim to recommend items that are relevant to the user's interest. Unfortunately, the recommended items will soon become too familiar to the user and hence fail to arouse her interest. Discovery-oriented ...
Comments