A preliminary study on a recommender system for the job recommendation challenge

In this paper we present our method used in the RecSys '16 Challenge. In particular, we propose a general collaborative filtering framework where many predictors can be cast. The framework is able to incorporate information about the content but in a collaborative fashion. Using this framework we instantiate a set of different predictors that consider different aspects of the dataset provided for the challenge. In order to merge all these aspects together, we also provide a method able to linearly combine the predictors. This method learns the weights of the predictors by solving a quadratic optimization problem. In the experimental section we show the performance using different predictors combinations. Results highlight the fact that the combination always outperforms the single predictor.