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New Ideas in Ranking for Personalized Fashion Recommender Systems

  • Heri RamampiaroEmail author
  • Helge Langseth
  • Thomas Almenningen
  • Herman Schistad
  • Martin Havig
  • Hai Thanh Nguyen
Chapter

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.

Keywords

Deterministic fashion recommender Goal-oriented recommendation Recommendation system Stochastic fashion recommender 

Notes

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.10

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Heri Ramampiaro
    • 1
    Email author
  • Helge Langseth
    • 1
  • Thomas Almenningen
    • 1
  • Herman Schistad
    • 1
  • Martin Havig
    • 1
  • Hai Thanh Nguyen
    • 1
    • 2
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Telenor ResearchTrondheimNorway

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