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New Ideas in Ranking for Personalized Fashion Recommender Systems

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Business and Consumer Analytics: New Ideas

Abstract

Fashion is an area that is in constant growth. The proliferation of social media and the Web, in general, has made e-shopping, thus corresponding recommender systems, increasingly important. Fashion recommender systems is a related area that we focus on in this chapter. More specifically, we present how recommender systems are used in online fashion stores to enhance the user experience and increase sales. In addition, we look at challenges the fashion domain specifically faces. We exemplify solution strategies by considering the SoBazaar system, including showing how we built a recommendation approach for the system and discussing results from our experiments. The results from these experiments demonstrate the effectiveness and viability of our method.

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Notes

  1. 1.

    See https://blog.kissmetrics.com/how-netflix-uses-analytics.

  2. 2.

    See http://www.myntra.com.

  3. 3.

    See http://www.asos.com.

  4. 4.

    See https://www.zalando.co.uk.

  5. 5.

    See http://www.mallzee.com.

  6. 6.

    See http://www.farfetch.com.

  7. 7.

    SoBazaar has been acquired by another company and is currently called Villoid (see http://www.villoid.com).

  8. 8.

    See http://mahout.apache.org.

  9. 9.

    SoBazaar has been acquired by VILLOID after our study was completed. The current version of the system can be found at http://villoid.com.

  10. 10.

    See http://opendatacommons.org/licenses/odbl/1.0/.

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Acknowledgements

We would like to thank the SoBazaar team at Telenor Digital for providing the SoBazaar dataset and thus making our experiments possible. The dataset is available at https://github.com/hainguyen-telenor/Learning-to-rank-from-implicit-feedback under the Open Database (ODbL) v1.0 Licence.Footnote 10

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Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

    Heri Ramampiaro, Helge Langseth, Thomas Almenningen, Herman Schistad, Martin Havig & Hai Thanh Nguyen

  2. Telenor Research, Trondheim, Norway

    Hai Thanh Nguyen

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  1. Heri Ramampiaro
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Correspondence to Heri Ramampiaro .

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Editors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Pablo Moscato

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries

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Ramampiaro, H., Langseth, H., Almenningen, T., Schistad, H., Havig, M., Nguyen, H.T. (2019). New Ideas in Ranking for Personalized Fashion Recommender Systems. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_25

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  • DOI: https://doi.org/10.1007/978-3-030-06222-4_25

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