Collaborative filtering (CF), one of the most successful recomendation approaches, continues to attract interest in both academia and industry. However, one key issue limiting the success of collaborative filtering in certain application domains is the cold-start problem, a situation where historical data is too sparce (known as the sparsity problem), new users have hot rated enough items (known as new user problem), or both. In this paper, we aim at addressing the cold-start problem by incorporating human personality into the collaborative filtering framework. We propose three approaches: the first is a recommendation method based on user`s personality information alone; the second is based on a linear combination of both personality and rating information; and the third uses a cascade mechanism to leverage both resources. To evaluate their effectiveness, we have conducted and experimental study comparing the proposed approaches with the tradicional rating-based CF in two cold-start scenarios: sparse data sets and new users. Our results show that the proposed CF variations, which consider personality characteristics, can significantly improve the performance of the traditional rating-based CF in terms of the evaluation metrics Mean Absolute Error (MAE) and Receiver Operating Characteristic (ROC) sensitivity.
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