Recommending Scientific Papers: Investigating the User Curriculum

In this paper, we propose a Personalized Paper Recommender System, a new user-paper based approach that takes into consideration the user academic curriculum vitae. To build the user profiles, we use a Brazilian academic platform called CV-Lattes. Furthermore, we examine some issues related to user profiling, such as (i) we define and compare different strategies to build and represent the user profiles, using terms and using concepts; (ii) we verify how much past information of a user is required to provide good recommendations; (iii) we compare our approaches with the state-of-art in paper recommendation using the CV-Lattes. To validate our strategies, we conduct a user study experiment involving 30 users in the Computer Science domain. Our results show that (i) our approaches outperform the state-of-art in CV-Lattes; (ii) concepts profiles are comparable with the terms profiles; (iii) analyzing the content of the past four years for terms profiles and five years for concepts profiles achieved the best results; and (iv) terms profiles provide better results but they are slower than concepts profiles, thus, if the system needs real time recommendations, concepts profiles are better.