Integrating user-side information into matrix factorization to address data sparsity of collaborative filtering

Recommendation techniques play a vital role in recommending an actual product to an intended user. The recommendation also supports the user in the decision-making process. In recent years, collaborative filtering has been a widely used technique in recommender systems. A model-based CF technique called matrix factorization fills the user–item interaction matrix’s missing elements. One of the significant challenges in MF is filling those elements in a row or column. The user has a few observations about an item, leading to sparsity issues of collaborative filtering. Therefore, conventional MF alone is not suitable to address the new item or user problem. We propose an MF model that integrates user-side information to handle these issues. We integrate user-side information in terms of vectors and bias to overcome the sparsity problem of collaborative filtering. We exhaustively evaluate our model on real-world datasets for predicting the ratings. The experiment results and analysis demonstrate that the proposed approach improves predictions significantly compared to the state-of-the-art techniques.