Best Next Preference Prediction Based on LSTM and Multi-level Interactions

Ivett Fuentes*, Gonzalo Nápoles, Leticia Arco, Koen Vanhoof

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Predict customer buying behavior is an important task for improving direct marketing campaigns, offering the best possible experiences, and providing personalization in the customer journey trip. Improving how models capture the sequential information from transactional data is essential to learn customer buying order and repetitive buying patterns to generate recommendations over time. In this paper, we propose the deep neural network approach DeepCBPP, which models the sequence prediction problem as a multi-class classification problem and takes the LSTM neural network as the base of the training process.
Original languageEnglish
Title of host publicationIntelligent Systems and Applications
Number of pages18
Publication statusPublished - 2022
EventIntelligent Systems Conference (IntelliSys) 2021 -
Duration: 2 Sept 2021 → …


ConferenceIntelligent Systems Conference (IntelliSys) 2021
Period2/09/21 → …


  • CustomeSequence representation
  • Sequence prediction models
  • LSTM
  • Customer buying behavior
  • Multi-class classification


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