Article ID: 2025EDP7030
Owing to the inherent sparsity of the user-item interaction matrix, the majority of existing collaborative filtering-based recommendation algorithms predominantly focus on the explicit interactions between users and items, thereby neglecting the complex interdependencies among items and users. This oversight results in a suboptimal representation of user and item characteristics, ultimately leading to a diminished quality of recommendations. To address this limitation, we proposed a novel recommendation algorithm, the Dual Co-occurrence Convolutional Neural Network (DCoCNN). DCoCNN innovatively integrates three pivotal components: user-item interactions, user-user co-occurrences, and item co-occurrences, leveraging the powerful feature extraction capabilities of CNN to train and refine latent features. Since items or users often emerge in pairs, DCoCNN thoroughly explores the intrinsic relationships among items or users, compensates for the lack of item-user interaction behaviors, and enables the trained latent features to contain more effective co-occurrence information, thereby enhancing model performance. The experimental results show that DCoCNN can effectively capture effective information between items or users, effectively mitigate the deficiencies with non-co-occurrence and single co-occurrence models, and improve recommendation quality.
