NRF: Normalized Rating Frequency for Collaborative Filtering Paper

Abstract

The online system is rapidly developing and widely used for companies to market their products. The products can always be more diverse and abundant. However, it creates difficulties for the company to provide recommendations to users about products which are suitable with users' interest. This condition encourages a recommendation system. One of the popular methods of this recommendation system is Collaborative Filtering (CF) by using rating-based and ranking-based approaches. Some of the ranking-based methods are Copeland method and Borda method. Both methods use a user-rating approach limited to the user preference profiling processes. Therefore, this research proposes the use of the user-rating to get the normalized rating frequency (NRF). Normalization process was done through the calculation of the frequency of a user-rating, which eventually generated a product ranking for recommendations to users. Experimental results of the NRF method can improve the performance of the recommendation system. This can be seen from the recommendations produced by the NRF method was more relevant in accordance with the wishes of the user, which is indicated by the average value of Normalized Discounted Cumulative Gain (NDCG) higher than Copeland and Borda methods. In addition, the NRF method has a faster computation time with a simpler algorithm than the Copeland and Borda methods.