Map-based Visualization of Item Spaces for Increasing Transparency and Control in Recommender Systems
Pages 695 - 699
Abstract
Recommender systems (RS) are very common tools designed to help users choose items from a large number of alternatives. While their algorithms are already quite mature in terms of precision, RS cannot unfold their full potential due to a lack of transparency and missing means of control. In this paper we introduce a method aiming at creating recommendations that are comprehensible and controllable by their users while granting an overview over the item domain. To achieve this, the entire item space of a domain is visualized using a map-like interface. Inside, users can express their preferences on which the RS reacts with matching recommendations. To change recommendations, users can alter their preferences expressed, which creates a continuous feedback loop between user and RS. We demonstrate our general method using two prototype applications, located in different item domains and utilizing different forms of visualization and interaction modalities. Empirical user studies with both prototypes show a great potential of our method to increase overview, transparency and control in RS.
References
[1]
2015. Blocked: When the information is hidden by the visualization. Journal of the Association for Information Science and Technology 67, 5 (2015), 1033--1051.
[2]
2018. Impact of Item Consumption on Assessment of Recommendations in User Studies. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, New York, NY, 49--53.
[3]
Shlomo Berkovsky, Ronnie Taib, und Dan Conway. 2017. How to Recommend?: User Trust Factors in Movie Recommender Systems. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI '17). ACM, New York, NY, USA, 287--300.
[4]
Jacques Bertin. 1983. Semiology of Graphics. University of Wisconsin Press.
[5]
Ingwer Borg und Patrick J. F. Groenen. 2005. Modern Multidimensional Scaling: Theory and Applications (2 ed.). Springer New York.
[6]
Svetlin Bostandjiev, John O'Donovan, und Tobias Höllerer. 2012. TasteWeights: A Visual Interactive Hybrid Recommender System. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys '12). ACM, New York, NY, USA, 35--42.
[7]
André Calero Valdez, Martina Ziefle, und Katrien Verbert. 2016. HCI for Recommender Systems: The Past, the Present and the Future. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 123--126.
[8]
Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, und Walter Quattrociocchi. 2016. The spreading of misinformation online. Proceedings of the National Academy of Sciences of the United States of America 113, 3 (2016), 554--559.
[9]
Dominic DiFranzo und Kristine Gloria-Garcia. 2017. Filter Bubbles and Fake News. XRDS 23, 3 (2017), 32--35.
[10]
Emden Gansner, Yifan Hu, Stephen Kobourov, und Chris Volinsky. 2009. Putting Recommendations on the Map - Visualizing Clusters and Relations. In Proceedings of the Third ACM Conference on Recommender Systems (RecSys '09). ACM, New York, NY, USA, 345--348.
[11]
Chen He, Denis Parra, und Katrien Verbert. 2016. Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications 56 (2016), 9--27.
[12]
Dietmar Jannach und Gediminas Adomavicius. 2016. Recommendations with a Purpose. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 7--10.
[13]
Michael Jugovac und Dietmar Jannach. 2017. Interacting with Recommenders--Overview and Research Directions. ACM Transactions on Interactive Intelligent Systems (TiiS) 7, 3 (2017), 10:1-10:46.
[14]
Mandy Keck und Dietrich Kammer. 2018. Exploring Visualization Challenges for Interactive Recommender Systems. In VisBIA 2018. http://ceur-ws.org/Vol-2108/paper3.pdf
[15]
Yehuda Koren, Robert M. Bell, und Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. IEEE Computer 42, 8 (2009), 30--37.
[16]
Johannes Kunkel, Tim Donkers, Lisa Michael, Catalin-Mihai Barbu, und Jürgen Ziegler. 2019. Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems. In CHI Conference on Human Factors in Computing Systems Proceedings. ACM, New York, NY, USA, to appear.
[17]
Johannes Kunkel, Benedikt Loepp, und Jürgen Ziegler. 2015. 3D-Visualisierung zur Eingabe von Präferenzen in Empfehlungssystemen. In Mensch und Computer 2015: Gemeinsam -- Arbeit -- Erleben.
[18]
Johannes Kunkel, Benedikt Loepp, und Jürgen Ziegler. 2017. A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI '17). ACM, New York, NY, USA, 3--15.
[19]
Johannes Kunkel, Benedikt Loepp, und Jürgen Ziegler. 2018. Understanding Latent Factors Using a GWAP. In Proceedings of the Late-Breaking Results track part of the Twelfth ACM Conference on Recommender Systems (RecSys'18). https://arxiv.org/pdf/1808.10260.pdf
[20]
Benedikt Loepp, Tim Donkers, Timm Kleemann, und Jürgen Ziegler. 2018. Interactive recommending with Tag-Enhanced Matrix Factorization (TagMF). International Journal of Human-Computer Studies (2018).
[21]
Benedikt Loepp, Tim Donkers, Timm Kleemann, und Jürgen Ziegler. 2019. Interactive recommending with Tag-Enhanced Matrix Factorization (TagMF). International Journal of Human-Computer Studies (2019), 21--41. Issue 121.
[22]
B. Németh, G. Takács, I. Pilászy, und D. Tikk. 2013. Visualization of movie features in collaborative filtering. In 2013 IEEE 12th International Conference on Intelligent Software Methodologies, Tools and Techniques (SoMeT). 229--233.
[23]
Eli Pariser. 2011. The filter bubble: What the Internet is hiding from you. Penguin UK.
[24]
Denis Parra und Peter Brusilovsky. 2015. User-controllable personalization: A case study with SetFusion. International Journal of Human-Computer Studies 78 (2015), 43--67.
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Published: 08 September 2019
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