A Mixed Collaborative Recommender System Using Singular Value Decomposition and Item Similarity

Nowadays, Recommendation system plays a vital role in industries like e-commerce, music apps or newsgroup, retailers, etc. Broadly, recommender system techniques are categorized into collaborative filtering and content based. In contrast, most recommendation models adopt collaborative filtering techniques such as matrix factorization (MF) and cosine similarity. However, the above model only deploys single techniques, which leads to poor recommendations to the individuals. Therefore we propose a mixed collaborative filtering-based recommender system (RS), a novel approach to improve the performance of the RS and mitigate the drawback of a single technique-based collaborative model. The proposed model is composed of two techniques such as singular value decomposition and cosine similarity techniques. Further, we examined our model's performance on real-world datasets and found that the proposed approach significantly outperformed the baseline models.