A model for opinion formation and anticipation based on the match between hidden personal “preferences” and product qualities is presented. We assume that products and individuals are represented by means of vectors in an L-dimensional “taste” space. The opinion of an individual on a given product is proportional to the overlap between the corresponding vectors. Assuming that both individual preferences and product qualities are hidden degrees of freedom, and that only the expressed opinion is observable, we use the correlations among individuals’ opinions on products to extract information about the hidden quantities. In particular, the method can be used to anticipate the opinion of an individual on a given product, to study the overlaps of preferences of two individuals, and to extract the dimensionality (L) of the hidden taste space.
De gustibus disputandum (forecasting opinions by knowledge networks) / F. BAGNOLI; F. FRANCI; A. BERRONES. - In: PHYSICA. A. - ISSN 0378-4371. - STAMPA. - 332:(2004), pp. 509-518. [10.1016/j.physa.2003.09.065]
De gustibus disputandum (forecasting opinions by knowledge networks)
BAGNOLI, FRANCO;FRANCI, FABIO;
2004
Abstract
A model for opinion formation and anticipation based on the match between hidden personal “preferences” and product qualities is presented. We assume that products and individuals are represented by means of vectors in an L-dimensional “taste” space. The opinion of an individual on a given product is proportional to the overlap between the corresponding vectors. Assuming that both individual preferences and product qualities are hidden degrees of freedom, and that only the expressed opinion is observable, we use the correlations among individuals’ opinions on products to extract information about the hidden quantities. In particular, the method can be used to anticipate the opinion of an individual on a given product, to study the overlaps of preferences of two individuals, and to extract the dimensionality (L) of the hidden taste space.File | Dimensione | Formato | |
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