Revving up Sales: Quantifying Automobile Relatedness and Predicting Next Purchase through Network Embeddings

Image credit: CCSS-LAB
This research project aims to enhance the decision-making process in the automotive industry. We analyzed the complete circulation permit data from 2016 to 2021 to establish a relational network structure of vehicles and their embedding representation. Then, we developed recommendation systems that were fine-tuned using customer data. Consequently, the project intends to provide valuable insights for decision-making that can cater to the needs of current and future customers.

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Cristian Candia
Cristian Candia
Head at CRiSS-LAB, School of Engineering and School of Government, Universidad del Desarrollo, Chile.

My research interests include collective behavior, collective and artificial, network science, and business analytics.