An Effective Similarity Measure for Neighborhood-based Collaborative Filtering

Thanks to its successful application in recommendation systems, collaborative filtering (CF) technique has become one of the most popular research topic in data mining and information retrieval. The two more dominant approaches to CF are neighborhood-based models and latent factor models. Currently, the state-of-the-art recommendation systems are mainly based on the latter approach owing to its ability to explore hidden factors connecting items to users. In this work, we propose a novel similarity measure which helps improve the accuracy of the neighborhood-based models. Intensive experiments show that our proposed model not only outperforms the best latent factor model with respect to RMSE by 2.2% but also works at least 2 times faster than its counterpart.