Elevating recommender systems: Cutting-edge transfer learning and embedding solutions

Abstract

In today’s information age and connected economy, Recommender Systems (RS) plays a vital role in managing information overload and delivering personalized suggestions to users. This paper introduces a multistage model that leverages multimodal data embedding and deep transfer learning to accurately capture user preferences and item characteristics, resulting in highly tailored recommendations. A key innovation in this model is the incorporation of an image dataset in the second phase, which addresses cold-start problems for new items by providing additional visual context. Our approach excels in overcoming challenges related to data sparsity and cold-start issues, thereby providing users with realistic and relevant product recommendations. To validate the effectiveness of the proposed model, we conducted extensive evaluations using three diverse datasets: data from Brazilian e-commerce platforms, the MovieLens 1M dataset, and the Amazon Product Review dataset. These evaluations involved comprehensive comparisons with standard RS methods to assess performance improvements. The results indicate that our proposed model significantly outperforms traditional RS techniques in terms of accuracy and reliability. Our model provides more accurate and meaningful recommendations by effectively addressing issues such as cold-start and data scarcity. Specifically, the model achieved Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) scores of 0.5883 and 0.4012, respectively, which demonstrate its superior performance metrics across all datasets tested.