Service Recommendations with Deep Learning: A Study on Neural Collaborative Engines

Background: The present paper aims to investigate the adoption of Neural Networks for recommendation systems and to propose Deep Learning architectures as advanced frameworks for designing Collaborative Filtering engines. Recommendation systems are data-driven infrastructures which are widely adopted to create effective and cutting-edge smart services, allowing to personalize the value proposition and adapt it to changes and variations in customers' preferences. Method: Our research represents an exploratory investigation on the adoption of Neural Networks for Recommendation Systems, inspired by the findings of a recent study on service science that highlighted the suitability of those models for designing cutting-edge recommenders capable of overcoming stable traditional benchmarks like the Singular Value Decomposition and the k-Nearest Neighbors algorithms. Following this study, we designed a more "complex" Feed-Forward Neural Network, trained on the "Movielens 100K" dataset using the Mean-Squared Error function to approximate the model loss generated and the Adaptive Moment Estimation algorithm (Adam) for the parameters optimization. Results: The results of this study demonstrate the primary role of Feed-Forward Neural Networks for designing advanced Collaborative recommenders, consolidating and even improving the outcomes of the work that inspired our research. Conclusion: Given these assumptions, we confirm the suitability of Feed-Forward Neural Networks as effective recommendation algorithms, laying the foundations for further studies in neural-based recommendation science.