A Multi-Criteria Collaborative Filtering Approach Using Deep Learning and Dempster-Shafer Theory for Hotel Recommendations

Abstract: This research proposes a novel hotel recommendation system that addresses the limitations of single-criterion ratings by utilizing a multi-criteria collaborative filtering approach. The system integrates matrix factorization with a deep neural network to predict individual criteria ratings and employs Dempster-Shafer Theory (DST) to handle uncertainty in those predictions. By aggregating multiple ratings using evidential reasoning, the system provides a robust overall hotel recommendation. Experiments conducted on a real-world TripAdvisor dataset show the proposed method achieves superior accuracy compared to traditional and state-of-the-art models in terms of MAE, RMSE, and Coefficient of Determination.

Keywords: Hotel recommendation system, multi-criteria collaborative filtering, deep learning, matrix factorization, Dempster- Shafer theory, evidential reasoning.