An enhanced user-based collaborative filtering recommendation system using the users’ latent relationships weighting utilization

Abstract

Nowadays, A Recommendation system is an important technique in the development of electronic-commerce services and the most concerned approaches used in a recommendation system is a collaborative filtering algorithm, which uses the preference of users to make predictions. However, it works poorly to handle the sparse data. There are several previous methods used to deal with the weakness of collaborative filtering techniques such as the row-sampling approximating singular value decomposition algorithm, but the results show their disadvantages in practical use. In this paper, we propose an enhanced user-based collaborative filtering algorithm using users' latent relationships weighting (CF-ULRW), which we have used in the predicted rating process. In the experiments, our proposed method is compared with the userbased collaborative filtering and the row-sampling approximating singular value decomposition. The experimental results show that our proposed method outperforms other methods with the same dataset.