Sparse Data in Recommender Systems: Challenges and Algorithmic Solutions

The availability of data can be a concern in recommender systems which is one of the main reasons that contribute towards prediction accuracy, model stability and personalization issue. User–item interaction matrices obtained from real-world deployments are often sparse with high percent sparsity, leading to inaccurate similarity calculation. This situation results in under-determined latent factor learning and high generalization error. Since their inception as recommendation models, these systems evolved rapidly with data availability. But getting complete user-item interaction data is practically not possible. To deal with the sparsity issue, a lot of work is done in the field of recommendation systems. In this paper, we provide an organized analytic synthesis of algorithmic methods suggested to tackle problems generated by sparsity. It covers a comparative study across collaborative filtering, matrix factorization improvements, deep learning architectures, and hybrid multi-modal systems. Real-world evidence suggests that traditional collaborative filtering becomes less effective in highly sparser regimes, while deep learning and hybrid approaches perform significantly better under extreme sparsity and cold-start conditions.