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.
|Title of host publication||Intelligent Systems and Applications|
|Number of pages||18|
|Publication status||Published - 2022|
|Event||Intelligent Systems Conference (IntelliSys) 2021 - |
Duration: 2 Sep 2021 → …
|Conference||Intelligent Systems Conference (IntelliSys) 2021|
|Period||2/09/21 → …|