| Abstract | Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, who are termed as “near neighbors" and recommendations for a user, are based on his near neighbors’ ratings. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as “Early Adopters", who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we (1) propose an algorithm that considers the eagerness shown by users to adopt products, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets. |
