Journal Article

TriDeepRec: a hybrid deep learning approach to content- and behavior-based recommendation systems

Ghadami, Amirhossein, Tran, Thomas

2024

Overview

Hybrid recommendation systems are increasingly crucial for businesses aiming to boost revenue and customer engagement. These systems integrate various algorithms, each with unique strengths, to outperform traditional recommendation methods. Our study introduces a novel hybrid recommendation system, TriDeepRec, which effectively combines content-based and behavior-based data to enhance recommendation accuracy. We first introduce a Convolutional Autoencoder-based Recommendation System (CAERS), designed to process content data and extract complex, meaningful patterns, translating these into predictive ratings. Notably, CAERS tackles the cold-start problem by leveraging content information alone, making it robust in scenarios where historical user interaction data are sparse or unavailable. Next, we incorporate Neural Collaborative Filtering (NCF), a deep learning approach, to analyze past user behavior and predict ratings. The outputs from CAERS and NCF are then integrated using a Multilayer Perceptron (MLP), a type of neural network, to generate the final recommendations. Our methodology employs three deep learning techniques to create TriDeepRec, a system capable of utilizing both past interactions and content attributes. We evaluate our system using two datasets, focusing on both error-based metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and ranking quality through Normalized Discounted Cumulative Gain (NDCG). These metrics highlight the system’s performance in both prediction accuracy and ranking relevance. The results indicate improvements over both the individual components and other leading models in the field. This demonstrates that TriDeepRec, by harnessing the strengths of both content and behavior data, provides a more accurate, reliable, and effective recommendation system.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Deep learning / Machine learning / Multilayer perceptrons / Neural networks / Ranking / Ratings / Recommender systems / Root-mean-square errors