María Emilia Charnelli

Recommender System Based on Latent Topics

Collaborative filtering is one of the most used techniques in recommender systems. The goal of this paper is to propose a new method that uses latent topics to model the items to be recommended. In this way, the ability to establish a similarity between these elements is incorporated, improving the performance of the recommendation made. The performance of the proposed method has been measured in two very different contexts, yielding satisfactory results. Finally, the conclusions and some future lines of work are included.

Academic performance of university students and its relation with employment

Educational Data Mining collects the various methods that allow extracting novelty and useful information from large data volumes in educational contexts. This paper describes the process used to, through Data Mining techniques, identify the most relevant characteristics in relation to student academic performance at the School of Computer Science of the National University of La Plata. The results obtained using the proposed method to process the information relating to regular and non-regular students at the UNLP allowed establishing interesting relationships in relation to student academic performance. Based on the obtained models it can be said that the fact that the student works does not mean that their academic performance decrease and young students that take several years to join the faculty have better performance if they express interest in getting a job.