Review-Based Recommender System Using Outer Product on CNN

The expansion of the e-commerce market has led to the challenge of information overload, necessitating the development of recommender systems. The recommender system aids users in decision-making by suggesting items that align with their preferences. However, conventional recommendation models rely solely on quantitative user behavior data, such as user ratings, and lead to limitations in recommendation performance due to the sparsity problem. To address these issues, recent research has leveraged convolutional neural networks (CNNs) to extract and incorporate semantic information from user reviews. However, several prior studies have a disadvantage in that they fail to account for the intricate interactions between users and items directly. In this study, we introduce a novel approach, the Review-based recommender system using Outer Product on CNN (ROP-CNN) model, which adeptly captures and incorporates semantic features from reviews to address the complex interactions between users and items using CNN. The experimental results, using real user-review datasets, demonstrate that the ROP-CNN model outperforms existing baseline models for prediction accuracy. And this study presents a novel theoretical and methodological perspective in recommendation research, suggesting a method that integrates user preference information from reviews into recommender systems by leveraging rich user-item interaction information.