Non-negative Matrix Factorization for Inferring Implicit Preference of Potential Customer

An agent model that infers a person´s implicit preference is proposed. The goal is to identify potential customers interested in various products or services of companies. As suggested by the mere exposure effect, the repeated exposure to stimuli increased the subjects´ positive attitude to repeated stimuli. To express a person´s preference, therefore, the agent extracts objects that appear frequently in the images of scenes that the person likely sees. If some extracted objects are related to a company´s business, the person is assumed to be its potential customer. Sparse Non-negative Matrix Factorization (SNMF) is introduced to extract unknown objects appearing in many images. The sparseness imposed on the coefficient matrix is related to the ability of a person to recognize objects, and it is controlled by one parameter. Experiments confirmed: (1) as the number of objects recognized at one time increased, the number of extracted objects increased. On the other hand, as the number decreased, similar objects were assumed to be the same. Thus, it is possible to infer preferences of persons with different levels of abilities for object recognition; (2) the sparseness condition is suitable for detecting multiple objects in one image; and (3) according to the level of the sparseness, the optimal number of objects that should be extracted is efficiently obtained by an adaptive gain control.