Emotional properties of latent factors in an image recommender system
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In this paper we analyze the relations between the latent factors with high variance description and affective parameters in an image recommender system. Using the matrix factorization approach we identify the main two factors in the user-item rating database. We exploit the affective metadata related to each item to identify relations between the main factors and the affective metadata. Results show that the first latent factor is strongly related with the valence and dominance while the arousal does not appear to be related. The second factor, however, shows no relation with the affective parameters.