Enhancing Diversity-Accuracy Technique on User-Based Top-N Recommendation Algorithms

In this paper we demonstrate how each item in top-N recommendation list has an impact on total diversity of the list in recommender systems. We proposed a new recommendation ranking method, namely "Total Diversity Effect Ranking", based on the total diversity effect of each item. Typically, diversity and accuracy in recommender systems are conversely. The finding shows that the proposed ranking method can guarantee the improvement of diversity quality in top-N recommendations. We proposed two new hybrid ranking approaches to make them more balance and designed experiments to evaluate their performance in comparison with the standard ranking approaches. To make results of our experiments more reliable, we apply the 5-fold cross validation technique to the MovieLens dataset provided by the GroupLens Research Project. In an evaluation process, we suggest using harmonic mean to measure the quality of recommendation approaches. The findings show that the proposed approaches give better performance than the standard approaches.