Lazy learning and sparsity handling in recommendation systems

Recommendation systems are ubiquitous in various domains, facilitating users in finding relevant items according to their preferences. Identifying pertinent items that meet their preferences enables users to target the right items. To predict ratings for more accurate forecasts, recommender systems often use collaborative filtering (CF) approaches to sparse user-rated item matrices. Due to a lack of knowledge regarding newly formed entities, the data sparsity of the user-rated item matrix has an enormous effect on collaborative filtering algorithms, which frequently face lazy learning issues. Real-world datasets with exponentially increasing users and reviews make this situation worse. Matrix factorization (MF) stands out as a key strategy in recommender systems, especially for CF tasks. This paper presents a neural network matrix factorization (NNMF) model through machine learning to overcome data sparsity challenges. This approach aims to enhance recommendation quality while mitigating the impact of data sparsity, a common issue in CF algorithms. A thorough comparative analysis was conducted on the well-known MovieLens dataset, spanning from 1.6 to 9.6 M records. The outcomes consistently favored the NNMF algorithm, showcasing superior performance compared to the state-of-the-art method in this domain in terms of precision, recall, \({\mathcal {F}}1_{\textrm{score}}\) $\mathcal{F}{1}_{\text{score}}$ , MAE, and RMSE.