Abstract. Most recommendation systems employ variations of Collaborative Filtering (CF) for formulating suggestions of items relevant to users' interests. However, CF requires expensive computations that grow polynomially with the number of users and items in the database. Methods proposed for handling this scalability problem and speeding up recommendation formulation are based on approximation mechanisms and, even when performance improves, they most of the time result in accuracy degradation. We propose a method for addressing the scalability problem based on incremental updates of user-to-user similarities. Our Incremental Collaborative Filtering (ICF) algorithm (i) is not based on any approximation method and gives the potential for high-quality recommendation formulation (ii) provides recommendations orders of magnitude faster than classic CF and thus, is suitable for online application.

Abstract. Recommender Systems (RS) are applications that provide personalized advice to users about products or services they might be interested in. To improve recommendation quality, many hybridization techniques have been proposed. Among all hybrids, the weighted recommenders have the main benefit that all of the system's constituents operate independently and stand in a straightforward way over the recommendation process. However, the hybrids proposed so far consist of a linear combination of the final scores resulting from all recommendation techniques available. Thus, they fail to provide explanations of predictions or further insights into the data. In this work, we propose a theoretical framework to combine information using the two basic probabilistic schemes: the sum and product rule. Extensive experiments have shown that our purely probabilistic schemes provide better quality recommendations compared to other methods that combine numerical scores derived from each prediction method individually.