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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2017: Advances in Knowledge Discovery and Data Mining pp 539–550Cite as

Modeling Contextual Changes in User Behaviour in Fashion e-Commerce

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10235)

Abstract

Impulse purchases are quite frequent in fashion e-commerce; browse patterns indicate fluid context changes across diverse product types probably due to the lack of a well-defined need at the consumer’s end. Data from our fashion e-commerce portal indicate that the final product a person ends-up purchasing is often very different from the initial product he/she started the session with. We refer to this characteristic as a ‘context change’. This feature of fashion e-commerce makes understanding and predicting user behaviour quite challenging. Our work attempts to model this characteristic so as to both detect and preempt context changes. Our approach employs a deep Gated Recurrent Unit (GRU) over clickstream data. We show that this model captures context changes better than other non-sequential baseline models.

Keywords

  • Product Group
  • Majority Vote
  • User Behaviour
  • Context Change
  • Mean Average Precision

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-57529-2_42
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Notes

  1. 1.

    Here sessions are all the products clicked by a user within a 30 min window.

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Authors and Affiliations

  1. Myntra, Bengaluru, India

    Ashay Tamhane, Sagar Arora & Deepak Warrier

Authors
  1. Ashay Tamhane
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  2. Sagar Arora
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  3. Deepak Warrier
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Corresponding author

Correspondence to Ashay Tamhane .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Tamhane, A., Arora, S., Warrier, D. (2017). Modeling Contextual Changes in User Behaviour in Fashion e-Commerce. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_42

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  • DOI: https://doi.org/10.1007/978-3-319-57529-2_42

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