As news selection is increasingly controlled by algorithms, a growing number of scholars are exploring how news recommenders can serve public services. Despite aspirations towards public service algorithms, little is known about which type of news recommender people prefer, let alone about a news recommender that aims to promote societal values. Th...
status: published
status: Published online
Published in Scientific Reports
Modern technology has drastically changed the way we interact and consume information. For example, online social platforms allow for seamless communication exchanges at an unprecedented scale. However, we are still bounded by cognitive and temporal constraints. Our attention is limited and extremely valuable. Algorithmic personalisation has become...
Matrix factorization with trace norm regularization is a popular approach to matrix completion and collaborative filtering. When entries of the matrix are sampled non-uniformly (which is the case for collaborative prediction), a properly weighted correction to the trace norm regularization is known to improve the performance dramatically. While the...
Despite the existence of dierent methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization i...
Memory-based collaborative filtering (CF) makes recommendations based on a collection of user preferences for items. The idea underlying this approach is that the interests of an active user will more likely coincide with those of users who share similar preferences to the active user. Hence, the choice and computation of a similarity measure betwe...
Memory-based collaborative filtering is the state-of-the-art method in recommender systems and has proven to be successful in various applications. In this paper we develop novel memory-based methods that incorporate the level of a user credit instead of using similarity between users. The user credit is the degree of one's rating reliability that ...
