Factorization Machines for Hybrid Recommendation Systems Based on Behavioral, Product, and Customer Data

Abstract

This study creates a hybrid recommendation system for online offer personalization of an e-commerce company. The system goes beyond existing literature by combining four different data sources, i.e. customer data, product data, implicit and explicit behavioral data, in a single algorithm. Factorization machines are employed as model-based algorithm and have as advantage that the four data sources are incorporated in a single model by feature combination. Results show that hybridization of the four distinct data sources improves accuracy compared to (i) factorization machines based on a single data source and (ii) a real-life company benchmark model using collaborative filtering.