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Modeling Contextual Changes in User Behaviour in Fashion e-Commerce

Part of the Lecture Notes in Computer Science book series (LNAI,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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    Here sessions are all the products clicked by a user within a 30 min window.


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Correspondence to Ashay Tamhane .

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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.

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