Shopper intent prediction from clickstream e-commerce data with minimal browsing information

Borja Requena, Giovanni Cassani, Jacopo Tagliabue, Ciro Greco, Lucas Lacasa

Research output: Contribution to journalArticleScientificpeer-review


We address the problem of user intent prediction from clickstream data of an e-commerce website via two conceptually different approaches: a hand-crafted feature-based classification and a deep learning-based classification. In both approaches, we deliberately coarse-grain a new clickstream proprietary dataset to produce symbolic trajectories with minimal information. Then, we tackle the problem of trajectory classification of arbitrary length and ultimately, early prediction of limited-length trajectories, both for balanced and unbalanced datasets. Our analysis shows that k-gram statistics with visibility graph motifs produce fast and accurate classifications, highlighting that purchase prediction is reliable even for extremely short observation windows. In the deep learning case, we benchmarked previous state-of-the-art (SOTA) models on the new dataset, and improved classification accuracy over SOTA performances with our proposed LSTM architecture. We conclude with an in-depth error analysis and a careful evaluation of the pros and cons of the two approaches when applied to realistic industry use cases.

Original languageEnglish
Pages (from-to)16983
Number of pages21
JournalScientific Reports
Issue number1
Publication statusPublished - 12 Oct 2020


  • intent prediction
  • neural networks
  • visibility graphs


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