Body measure-aware fashion product recommendations: evaluating the predictive power of body scan data

Piazza A, Süßmuth J, Bodendorf F (2017)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2017

Publisher: CEUR-WS.org

City/Town: Aachen

Pages Range: 5-8

Conference Proceedings Title: Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios co-located with 11th ACM Conference on Recommender Systems (RecSys 2017)

Event location: Como, Italy IT

Open Access Link: http://ceur-ws.org/Vol-1892/paper1.pdf

Abstract

Fashion product consumer are faced with large and fast changing product offerings. The fashion purchase decision process is complex, as the consumer has to consider various inuencing factors like current fashion trends, what fashion products fit to their personality, and what products fit to their physical appearance like hair colors or body measures. Based on novel technologies, 3D body avatars can be reconstructed from 3D or 2D data. From these avatars, body measures can be determined. The objective of this research is to investigate the predictive performance of body measures extracted from a 3D body scanner for predicting fashion item preferences. Therefore, item preferences and body scans from 200 users were collected. From the body scans, 11 body measures are extracted and integrated into a prediction model using Factorization Machines.The results from a cross-validation show, that including body measurements signicantly improves the prediction performance of the recommendation model, especially in new user scenarios, when no information about the fashion product preferences of the active user is known.

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How to cite

APA:

Piazza, A., Süßmuth, J., & Bodendorf, F. (2017). Body measure-aware fashion product recommendations: evaluating the predictive power of body scan data. In Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin (Eds.), Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios co-located with 11th ACM Conference on Recommender Systems (RecSys 2017) (pp. 5-8). Como, Italy, IT: Aachen: CEUR-WS.org.

MLA:

Piazza, Alexander, Jochen Süßmuth, and Freimut Bodendorf. "Body measure-aware fashion product recommendations: evaluating the predictive power of body scan data." Proceedings of the Workshop on Recommendation in Complex Scenarios co-located with 11th ACM Conference on Recommender Systems (RecSys 2017), Como, Italy Ed. Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin, Aachen: CEUR-WS.org, 2017. 5-8.

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