Abstract
Recommenders play a significant role in our daily lives, making decisions for users on a regular basis. Their widespread adoption necessitates a thorough examination of how users interact with recommenders and the algorithms that drive them. An important form of interaction in these systems are algorithmic affordances: means that provide users with perceptible control over the algorithm by, for instance, providing context (‘find a movie for this profile’), weighing criteria (‘most important is the main actor’), or evaluating results (‘loved this movie’). The assumption is that these algorithmic affordances impact interaction qualities such as transparency, trust, autonomy, and serendipity, and as a result, they impact the user experience. Currently, the precise nature of the relation between algorithmic affordances, their specific implementations in the interface, interaction qualities, and user experience remains unclear. Subjects that will be discussed during the workshop, therefore, include but are not limited to the impact of algorithmic affordances and their implementations on interaction qualities, balances between cognitive overload and transparency in recommender interfaces containing algorithmic affordances; and reasons why research into these types of interfaces sometimes fails to cross the research-practice gap and are not landing in the design practice. As a potential solution the workshop committee proposes a library of examples of algorithmic affordances design patterns and their implementations in recommender interfaces enriched with academic research concerning their impact. The final part of the workshop will be dedicated to formulating guiding principles for such a library.
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References
Resnick, P., Varian, H.R: Recommender systems. Commun. ACM 40(3), 56–58 (1997). https://doi.org/10.1145/245108.245121
Gunawardana, A., Shani, G., Yogev, S.: Evaluating recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 547–601. Springer, New York, NY (2022). https://doi.org/10.1007/978-1-0716-2197-4_15
Jugovac, M., Jannach, D.: Interacting with recommenders—overview and research directions. ACM Trans. Interact. Intell. Syst. 7(3), 1–46 (2017). https://doi.org/10.1145/3001837
Ghori, M., Dehpanah, A., Gemmell, J., Qahri-Saremi, H., Mobasher, B.: Does the user have a theory of the recommender? A grounded theory study. In: 2021 Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, pp. 167–174. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3511047.3537680
Hekman, E., Nguyen, D., Stalenhoef, M., Van Turnhout, K.: Towards a pattern library for algorithmic affordances. In: Joint Proceedings of the IUI 2022 Workshops, vol. 3124, pp. 24–33. (2022). https://ceur-ws.org/Vol-3124/paper3.pdf
Ngo, T., Kunkel, J., Ziegler, J.: Exploring mental models for transparent and controllable recommender systems: a qualitative study. In: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization 2020, pp. 183–191. Association for Computing Machinery, New York, NY (2020). https://doi.org/10.1145/3340631.3394841
Februari, M.: Doe zelf normaal: Menselijk recht in tijden van datasturing en natuurgeweld. Prometheus, Amsterdam (2023)
He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. In: Expert Systems with Applications, vol. 56, pp 9–27. (2016). https://doi.org/10.1016/j.eswa.2016.02.013
Dietvorst, B., Simmons, J., Massey, C.: Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them. Manage. Sci. 64(3), 1155–1170 (2018). https://doi.org/10.1287/mnsc.2016.2643
Shneiderman, B.: Human-Centered AI. Oxford University Press, Oxford (2022)
Tintarev, N., Masthoff, J.: Explaining recommendations: design and evaluation. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 353–382. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_10
Zhang, Y, Chen, X.: Explainable recommendation: a survey and new perspectives. Found. Trends Inf. Retrieval 14(1), 1–101 (2020). https://doi.org/10.1561/1500000066
Eslami, M., et al.: First I” like” it, then I hide it: Folk Theories of Social Feeds. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 2371–2382. Association for Computing Machinery, New York, NY (2016). https://doi.org/10.1145/2858036.2858494
Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems 2011, pp. 157–164. Association for Computing Machinery, New York (2011). https://doi.org/10.1145/2043932.2043962
Kulesza, T., Stumpf, S., Burnett, M., Kwan, I.: Tell me more?: The effects of mental model soundness on personalizing an intelligent agent. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1–10 (2012). https://doi.org/10.1145/2207676.2207678
Smits, A., Van Turnhout, K.: Towards a practice-led research agenda for user interface design of recommender systems. In: Human-Computer Interaction–INTERACT 2023: 19th IFIP TC 13 International Conference, York, United Kingdom, 28 August–1 September 2023
Turnhout, K., Smits, A.: Solution repertoire. In: Grierson, H., Bohemia, R., Buck, L. (eds) Proceedings of the 23rd International Conference on Engineering and Product Design Education (2021). https://doi.org/10.35199/EPDE.2021.41
Höök, K., Löwgren J.: Strong concepts: intermediate-level knowledge in interaction design research. ACM Trans. Comput. Hum. Interact. (TOCHI) 19(3), 1–18 (2012). https://doi.org/10.1145/2362364.2362371
Zielhuis, M., Visser, F., Andriessen, D., Stappers, P.: Making design research relevant for design practice: what is in the way? Des. Stud. 78(101063), 1–21 (2022). https://doi.org/10.1016/j.destud.2021.101063
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Smits, A., Bartels, E., Detweiler, C., van Turnhout, K. (2023). Algorithmic Affordances in Recommender Interfaces. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14145. Springer, Cham. https://doi.org/10.1007/978-3-031-42293-5_80
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