Exploring the effects of different Clustering Methods on a News Recommender System

News recommendations distinguishes from general content recommendations as it takes in consideration news freshness, sparsity, monotony and time. Recent works approach these features using hybrid Collaborative-Content-based Filtering methods, adapting clustering techniques to handle sparsity and monotony without considering the effects that different clustering methods may have over recommendation results. Such studies often evaluate the results of varying different parameters individually, ignoring possible interaction effects between them. They also base their results on metrics such as accuracy and recall that are sensitive to bias. To investigate the importance of clustering method selection to News Recommender System results we evaluated the effects of different traditional techniques in recommending news articles. We implemented an algorithm that used a hybrid Collaborative-Content-based Filtering method to incorporate user behavior, user interest, article popularity and time effect. The system uses an article selection method that built the recommendation set based on content features. With this algorithm, we examined the existence of interaction effects between the input parameters. We used a Gaussian regression process to explore the response surface while sequentially optimizing parameters. To avoid being misled by underlying biases we used Informedness, an accuracy metric that captures both positive and negative information from prediction results. Our results demonstrated that different clustering methods had a significant influence on the recommendation results. It was also found that a traditional hierarchical method outperformed optimization methods with important performance improvement. In addition, we demonstrated that parameters may interact with each other and that analyzing them separately may mislead interpretation.