Predicting outcomes of active sessions using multi-action motifs

Weiqiang Lin, Natasa Milic-Frayling, Ke Zhou, Eugene Ch’ng

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Web sites and online services increasingly engage with users through live chats to provide support, advice, and offers. Such approaches require reliable methods to predict the user’s intent and make an informed decision when and how to intervene during an active session. Prior work on predicting purchase intent involved clickstream data mining and feature construction in an ad-hoc manner with a moderate success (AUC 0.70 range). We demonstrate the use of the consumer Purchase Decision Model (PDM) and a principled way of constructing features predictive of the purchase intent. We show that the Logistic Regression (LR) classifiers, trained with multi-action motifs, perform on par with the state-of-the-art LSTM sequence model achieving comparable AUC (0.95 vs 0.96) and performing better for the sparse purchase sessions, with higher recall (0.85 vs 0.61) and higher F1 score (0.73 vs 0.66). While LSTM performs better than LR in terms of weighted averages of F1, precision, and recall, it requires 7 times longer to train and offers no insights about the predictive model in terms of the user actions and the purchase decision stages. The LR predictors are robust and effective in simulating real-time interventions, achieving F1 of 0.84 and AUC of 0.85 after observing only 50% of an active session. For non-purchase sessions that leaves room for live intervention, on average within 8 actions before the session ends.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
EditorsPayam Barnaghi, Georg Gottlob, Yannis Manolopoulos, Theodoros Tzouramanis, Athena Vakali
PublisherAssociation for Computing Machinery, Inc
Pages9-17
Number of pages9
ISBN (Electronic)9781450369343
DOIs
Publication statusPublished - 14 Oct 2019
Event19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019 - Thessaloniki, Greece
Duration: 13 Oct 201917 Oct 2019

Publication series

NameProceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019

Conference

Conference19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
Country/TerritoryGreece
CityThessaloniki
Period13/10/1917/10/19

Keywords

  • Action motifs
  • Consumer e-purchase
  • Purchase sessions
  • User behavior
  • User modelling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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