n 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.

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

POLATO, MIRKO;AIOLLI, FABIO
2016

Abstract

n 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.
2016
RecSys Challenge '16 Proceedings of the Recommender Systems Challenge
ACM RecSys 2016
9781450348010
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3220265
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