A Collaborative Filtering Recommendation System for Rating Prediction
Recommendation system is a subclass of information filtering system to help users find relevant items of interest from a large set of possible selections. Model-based collaborative filtering utilized the ratings of the user–item matrix dataset to generate a prediction. Essentially, this type of intelligent system plays a critical role in e-commerce, social network, and popular domains increasingly. In this research work, we present the comparison of the two widely used efficient techniques such as Biased Matrix Factorization and a regular Matrix Factorization, both using Stochastic Gradient Descent (SGD). We have conducted experiments on two real-world public datasets: Book Crossing and Movie Lens 100 K and evaluated by two metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Our experiments demonstrated that Biased Matrix Factorization used SGD technique results in a substantial increase in recommendation accuracy for rating prediction in experimental both datasets. Compute with a regular Matrix Factorization technique, Biased Matrix Factorization produced the reduction of the RMSE by 25.78% and MAE by 19.69% for Book Crossing dataset and RMSE by 19.69% and MAE by 14.08% for Movie Lens 100 K dataset. As expected when comparing the results of different datasets, Biased Matrix Factorization using SGD materialize less prediction error.
KeywordsModel-based collaborative filtering Matrix factorization Bias Stochastic gradient descent
This research was supported by the Private Intelligence Information Service Expansion (No. C0511-18-1001) funded by the NIPA (National IT Industry Promotion Agency) and supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2017R1A2B4010826).
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