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Synchronized Multi-list User Interfaces for Fashion Catalogs

Published: 16 June 2023 Publication History

Abstract

Several online catalogs use carousels to present thematic lists of products, based on different optimization criteria. While this makes it possible to search for items according to diverse relevance perspectives, it hardly supports an integrated evaluation, which is key to critical consuming behavior. To address this issue, we propose a synchronized multi-list model that (i) enriches item presentation by visualizing its evaluation and (ii) enables the user to simultaneously center the carousels of the multi-list on the item in her/his focus of attention, showing its ranking in each list. This type of visualization is aimed at enhancing the transparency of results by enabling the user to simultaneously compare products across all the evaluation criteria applied within the multi-list.
As a testbed for our model, we selected fashion catalogs, with the aim of making users aware of clothes’ evaluation with respect to the sustainability and ethical issues concerning the production practices applied by their brands. In a preliminary user study, we analyzed users’ gaze behavior to reveal how people interact with the carousels of the multi-list for product comparison. The results show that people explored the position of items in all the carousels, following a pattern that differs from the top-left triangle observed in traditional multi-lists, and they selected items having a fairly good ranking, showing their interest in sustainability and ethical standards.

References

[1]
Catalin-Mihai Barbu and Jürgen Ziegler. 2017. Towards a Design Space for Personalizing the Presentation of Recommendations. In Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2017), Lisbon, Portugal, June 26, 2017(CEUR Workshop Proceedings, Vol. 1945), Ludovico Boratto, Salvatore Carta, and Gianni Fenu (Eds.). CEUR-WS.org, 10–17. https://ceur-ws.org/Vol-1945/paper_3.pdf
[2]
Mustafa Bilgic and Mooney Raymond J.2005. Explaining recommendations: satisfaction vs. promotion. In Proceedings of the Workshop Beyond Personalization, in Conjunction with the International Conference on Intelligent User Interfaces (San Diego, CA, USA). Association for Computing Machinery, New York, NY, USA, 13–18. http://www.cs.iit.edu/ ml/pdfs/bilgic-iui05-wkshp.pdf
[3]
Flavio Chierichetti, Ravi Kumar, and Prabhakar Raghavan. 2011. Optimizing Two-Dimensional Search Results Presentation. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (Hong Kong, China) (WSDM ’11). Association for Computing Machinery, New York, NY, USA, 257–266. https://doi.org/10.1145/1935826.1935873
[4]
Ayoub El Majjodi, Alain D. Starke, and Christoph Trattner. 2022. Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (Barcelona, Spain) (UMAP ’22). Association for Computing Machinery, New York, NY, USA, 48–56. https://doi.org/10.1145/3503252.3531312
[5]
Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (dec 2016), 19 pages. https://doi.org/10.1145/2843948
[6]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, 770–778. https://doi.org/10.1109/CVPR.2016.90
[7]
Rong Hu and Pearl Pu. 2011. Enhancing Recommendation Diversity with Organization Interfaces. In Proceedings of the 16th International Conference on Intelligent User Interfaces (Palo Alto, CA, USA) (IUI ’11). Association for Computing Machinery, New York, NY, USA, 347–350. https://doi.org/10.1145/1943403.1943462
[8]
Mathias Jesse and Dietmar Jannach. 2021. Digital nudging with recommender systems: Survey and future directions. Computers in Human Behavior Reports 3 (2021), 100052. https://doi.org/10.1016/j.chbr.2020.100052
[9]
Annamma Joy, John F. Sherry Jr, Alladi Venkatesh, Jeff Wang, and Ricky Chan. 2012. Fast Fashion, Sustainability, and the Ethical Appeal of Luxury Brands. Fashion Theory 16, 3 (2012), 273–295. https://doi.org/10.2752/175174112X13340749707123
[10]
Dennis Lawo, Thomas Neifer, Margarita Esau, and Gunnar Stevens. 2021. Buying the ‘Right’ Thing: Designing Food Recommender Systems with Critical Consumers. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 85, 13 pages. https://doi.org/10.1145/3411764.3445264
[11]
Xin Liu, Jiancheng Li, Jiaqi Wang, and Ziwei Liu. 2021. MMFashion: An Open-Source Toolbox for Visual Fashion Analysis. In Proceedings of the 29th ACM International Conference on Multimedia (Virtual Event, China) (MM ’21). Association for Computing Machinery, New York, NY, USA, 3755–3758. https://doi.org/10.1145/3474085.3478327
[12]
Netflix.com. 2022. Netflix.com: subscription video-on-demand streaming service and production company.https://www.netflix.com/.
[13]
Kirsi Niinimäki, Greg Peters, Helena Dahlbo, Patsy Perry, Timo Rissanen, and Alison Gwilt. 2020. The environmental price of fast fashion. Nature Reviews Earth & Environment 1, 4 (2020), 189–200. https://doi.org/10.1038/s43017-020-0039-9
[14]
Alexandra Papoutsaki, Patsorn Sangkloy, James Laskey, Nediyana Daskalova, Jeff Huang, and James Hays. 2016. WebGazer: Scalable Webcam Eye Tracking Using User Interactions. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). AAAI, 3839–3845.
[15]
Pearl Pu and Li Chen. 2007. Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20, 6 (2007), 542 – 556. https://doi.org/10.1016/j.knosys.2007.04.004
[16]
Gaurav Sharma, Wencheng Wu, and Edul N. Dalal. 2004. The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. COLOR Research and Applications 30, 1 (2004), 21–30. https://doi.org/10.1002/col.20070
[17]
Alain Starke, Edis Asotic, and Christoph Trattner. 2021. “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface. In Proceedings of the 15th ACM Conference on Recommender Systems (Amsterdam, Netherlands) (RecSys ’21). Association for Computing Machinery, New York, NY, USA, 124–132. https://doi.org/10.1145/3460231.3474232
[18]
Liang Wu, Mihajlo Grbovic, and Jundong Li. 2021. Toward User Engagement Optimization in 2D Presentation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM ’21). Association for Computing Machinery, New York, NY, USA, 1047–1055. https://doi.org/10.1145/3437963.3441749
[19]
Good On You. 2022. Good On You - Sustainable and Ethical Fashion Brand Ratings. https://goodonyou.eco/.
[20]
Good On You. 2022. How we rate - goodonyou. https://goodonyou.eco/how-we-rate/.
[21]
Qian Zhao, Shuo Chang, F. Maxwell Harper, and Joseph A. Konstan. 2016. Gaze Prediction for Recommender Systems. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 131–138. https://doi.org/10.1145/2959100.2959150

Cited By

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  • (2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
  • (2024)Promoting Green Fashion Consumption Through Digital Nudges in Recommender SystemsIEEE Access10.1109/ACCESS.2024.334971012(6812-6829)Online publication date: 2024
  • (2023)Enriching Recommender Systems Results with Data about Sustainability and Ethical Standards of Brands2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00037(238-242)Online publication date: 26-Oct-2023

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Published In

cover image ACM Conferences
UMAP '23 Adjunct: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
June 2023
446 pages
ISBN:9781450398916
DOI:10.1145/3563359
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 16 June 2023

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Author Tags

  1. Environmental Sustainability
  2. Ethics
  3. Recommender Systems
  4. Transparent User Interfaces

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Cited By

View all
  • (2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
  • (2024)Promoting Green Fashion Consumption Through Digital Nudges in Recommender SystemsIEEE Access10.1109/ACCESS.2024.334971012(6812-6829)Online publication date: 2024
  • (2023)Enriching Recommender Systems Results with Data about Sustainability and Ethical Standards of Brands2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00037(238-242)Online publication date: 26-Oct-2023

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