Modeling Contextual Changes in User Behaviour in Fashion e-Commerce

  • Ashay TamhaneEmail author
  • Sagar Arora
  • Deepak Warrier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)


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.


Product Group Majority Vote User Behaviour Context Change Mean Average Precision 
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.MyntraBengaluruIndia

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