Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback

  • Hai Thanh Nguyen
  • Thomas Almenningen
  • Martin Havig
  • Herman Schistad
  • Anders Kofod-Petersen
  • Helge Langseth
  • Heri Ramampiaro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)

Abstract

Fashion e-commerce is a fast growing area in online shopping. The fashion domain has several interesting properties, which make personalised recommendations more difficult than in more traditional domains. To avoid potential bias when using explicit user ratings, which are also expensive to obtain, this work approaches fashion recommendations by analysing implicit feedback from users in an app. A user’s actual behaviour, such as Clicks, Wants and Purchases, is used to infer her implicit preference score of an item she has interacted with. This score is then blended with information about the item’s price and popularity as well as the recentness of the user’s action wrt. the item. Based on these implicit preference scores, we infer the user’s ranking of other fashion items by applying different recommendation algorithms. Experimental results show that the proposed method outperforms the most popular baseline approach, thus demonstrating its effectiveness and viability.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hai Thanh Nguyen
    • 1
  • Thomas Almenningen
    • 2
  • Martin Havig
    • 2
  • Herman Schistad
    • 2
  • Anders Kofod-Petersen
    • 1
    • 2
  • Helge Langseth
    • 2
  • Heri Ramampiaro
    • 2
  1. 1.Telenor ResearchTrondheimNorway
  2. 2.Department of Computer and Information Science (IDI)Norwegian University of Science and Technology (NTNU)TrondheimNorway

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