A Triangle Multi-level Item-Based Collaborative Filtering Method that Improves Recommendations

One of the most successful approaches that can provide a relevant recommendation in various domains is collaborative filtering. Although this approach has been widely applied, there are still limitations to be overcome in this research area. Accuracy is still one of the areas that need to be improved. In addition, the rapid growth of information available online presents recommender systems with several challenges. More specifically, data sparsity and coverage affect the quality of the recommendations that can be provided. In this paper, we propose an item-based collaborative filtering (IBCF) approach with triangle similarity measures that take into account the length and angle of rating vectors between users and allow positive and negative adjustments using a multi-level recommendation approach. We have improved the predictive accuracy and effectiveness of the proposed method, which outperforms all the compared methods in terms of the mean absolute error (MAE) and the root mean squared error (RMSE). We aimed to evaluate the proposed method by comparing our results with those of some popular similarity measures using k-nearest neighbour (kNN) algorithms. We ran our experiment using three real dataset: MovieLens 100K, MovieLens 1M and Yahoo! Movies.