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.

新闻推荐不同于一般的内容推荐，因为它考虑了新闻的新鲜度、稀疏性、单调性和时间。最近的工作使用基于协作内容的混合过滤方法来处理这些特征，在不考虑不同聚类方法可能对推荐结果产生的影响的情况下，采用聚类技术来处理稀疏性和单调性。此类研究通常单独评估不同参数的结果，忽略它们之间可能的交互作用。他们的结果还基于对偏差敏感的准确度和召回率等指标。为了研究聚类方法选择对新闻推荐系统结果的重要性，我们评估了不同传统技术在推荐新闻文章中的效果。我们实现了一种算法，该算法使用基于协作内容的混合过滤方法来整合用户行为、用户兴趣、文章流行度和时间效应。系统采用基于内容特征构建推荐集的文章选择方法。通过该算法，我们检查了输入参数之间是否存在交互作用。我们使用高斯回归过程来探索响应面，同时依次优化参数。为了避免被潜在偏见误导，我们使用了 Informedness，这是一种从预测结果中捕获正面和负面信息的准确度指标。我们的结果表明，不同的聚类方法对推荐结果有显着影响。还发现传统的分层方法优于优化方法，具有重要的性能改进。此外，我们证明了参数可能会相互影响，单独分析它们可能会误导解释。