Melanie Heck
ABSTRACT
User interfaces in self-order terminals aim to satisfy the need for information of a broad audience and thus get easily clut-tered. Online shops present personalized product recommen-dations based on previously gathered user data to channel the user's attention. In contrast, stateless point-of-sales machines generally have no access to the user's personal information nor previous purchase behavior. User preferences must therefore be determined during the interaction. We thus propose using gaze data to determine preferences in real-time. In this paper we present a system for dynamic gaze-based fltering.
- Hyejin Bang and Bartosz W. Wojdynski. 2016. Tracking Users' Visual Attention and Responses to Personalized Advertising Based on Task Cognitive Demand. Comput. Hum. Behav. 55, PB (Feb. 2016), 867--876. http://dx.doi.org/10.1016/j.chb.2015.10.025Google Scholar
Digital Library
- Georg Buscher, Edward Cutrell, and Meredith Ringel Morris. 2009. What Do You See when You'Re Surfing?: Using Eye Tracking to Predict Salient Regions of Web Pages. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '09). ACM, New York, NY, USA, 21--30. http://dx.doi.org/10.1145/1518701.1518705Google ScholarDigital Library
- Li Chen and Feng Wang. 2016. An Eye-Tracking Study: Implication to Implicit Critiquing Feedback Elicitation in Recommender Systems. In Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP '16). 163--167. http://dx.doi.org/10.1145/2930238.2930286Google ScholarDigital Library
- Haibin Cheng and Erick Cantú-Paz. 2010. Personalized Click Prediction in Sponsored Search. In Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM '10). 351--360. http://dx.doi.org/10.1145/1718487.1718531Google ScholarDigital Library
- Shiwei Cheng and Ying Liu. 2012. Eye-tracking based adaptive user interface: Implicit human-computer interaction for preference indication . Journal on Multimodal User Interfaces 5, 1--2 (2012), 77--84. http://dx.doi.org/10.1007/s12193-011-0064--6Google ScholarCross Ref
- Melih Kandemir, Veli-Matti Saarinen, and Samuel Kaski. 2010. Inferring Object Relevance from Gaze in Dynamic Scenes. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications (ETRA '10). 105--108. http://dx.doi.org/10.1145/1743666.1743692Google ScholarDigital Library
- Arto Klami, Craig Saunders, Teófilo E. de Campos, and Samuel Kaski. 2008. Can Relevance of Images Be Inferred from Eye Movements?. In Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval (MIR '08). 134--140. http://dx.doi.org/10.1145/1460096.1460120Google ScholarDigital Library
- P"a ivi Majaranta and Andreas Bulling. 2014. Eye Tracking and Eye-Based Human--Computer Interaction . In Advances in Physiological Computing. Human--Computer Interaction Series, S. Fairclough and K. Gilleade (Eds.). Springer, London, Chapter 3, 39--65. http://dx.doi.org/10.1007/978--1--4471--6392--3_3Google Scholar
- Thorsten Prante, Norbert A. Streitz, Carsten Rö cker, Richard Stenzel, Carsten Magerkurth, Daniel van Alphen, and Daniela A. Plewe. 2003. Hello.Wall - Beyond Ambient Displays. In Proceedings of the Fifth International Conference on Ubiquitous Computing (UBICOMP'03). 277 -- 278.Google Scholar
- B. Schilit, N. Adams, and R. Want. 1994. Context-Aware Computing Applications. In Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications (WMCSA '94). 85--90. http://dx.doi.org/10.1109/WMCSA.1994.16Google ScholarDigital Library
- Christina Schweikert, Louis Gobin, Shuxiao Xie, Shinsuke Shimojo, and D. Frank Hsu. 2018. Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion. In Proceedings of the International Conference on Brain Informatics. 282--293. http://dx.doi.org/10.1007/978--3-030-05587--5_27Google Scholar
- Hyejin Song and Nammee Moon. 2019. Eye-tracking and Social Behavior Preference-based Recommendation System . Journal of Supercomputing 75, 4 (2019), 1990--2006. http://dx.doi.org/10.1007/s11227-018--2447-xGoogle ScholarCross Ref
- S. Shyam Sundar and Sampada S. Marathe. 2010. Personalization versus customization: The importance of agency, privacy, and power usage . Human Communication Research 36, 3 (2010), 298--322. http://dx.doi.org/10.1111/j.1468--2958.2010.01377.xGoogle ScholarCross Ref
- Tore Vesterby, Jonas C. Voss, John Paulin Hansen, Arne John Glenstrup, Dan Witzner Hansen, and Mark Rudolph. 2005. Gaze-guided Viewing of Interactive Movies . Digital Creativity 16, 4 (2005), 193--204. http://dx.doi.org/10.1080/14626260500476523Google ScholarCross Ref
- Daniel Vogel and Ravin Balakrishnan. 2004. Interactive Public Ambient Displays: Transitioning from Implicit to Explicit, Public to Personal, Interaction with Multiple Users. In Proceedings of the 17th Annual ACM Symposium on User Interface Software and Technology (UIST '04). 137--146. http://dx.doi.org/10.1145/1029632.1029656Google ScholarDigital Library
- Miaosen Wang, Sebastian Boring, and Saul Greenberg. 2012. Proxemic Peddler: A Public Advertising Display That Captures and Preserves the Attention of a Passerby. In Proceedings of the 2012 International Symposium on Pervasive Displays (PerDis '12). Article 3, 6 pages. http://dx.doi.org/10.1145/2307798.2307801Google ScholarDigital Library
- Songhua Xu, Hao Jiang, and Francis C.M. Lau. 2008. Personalized Online Document, Image and Video Recommendation via Commodity Eye-tracking. In Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys '08). 83--90. http://dx.doi.org/10.1145/1454008.1454023Google ScholarDigital Library
Index Terms
- Gaze-based Product Filtering: A System for Creating Adaptive User Interfaces to Personalize Stateless Point-of-Sale Machines
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