An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing apparatus, comprising: a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section. 2. The information processing apparatus of claim 1, wherein the learning processing section further learns the feature value for the feature possessed by each of a plurality of the selection objects in the choice model. 3. The information processing apparatus of claim 1, further comprising a feature value acquiring section configured to acquire the feature value for the feature possessed by each of a plurality of the selection objects. 4. The information processing apparatus of claim 1, wherein: the history data includes a history indicating that at least one selection subject has selected a selection object in each of the plurality of selection environments; and the learning processing section allows the choice model to learn, where the choice model uses the environmental dependence of each selection object common to the plurality of selection subjects in each selection environment. 5. The information processing apparatus of claim 1, wherein the learning processing section allows the choice model to learn, where the choice model uses a feature vector indicative of a plurality of feature values corresponding to a plurality of features of each selection object, a preference vector of each selection subject indicative of a preference for each of the plurality of features, and an environment-dependent vector for each selection environment indicative of an environmental dependence of selection of each selection object. 6. The information processing apparatus of claim 5, wherein the learning processing section allows the choice model to learn, where the choice model calculates the selectability, with which each selection subject selects each selection object in each selection environment, based on a product of the feature vector of the selection object and the preference vector of the selection subject, and an element corresponding to the selection object in the environment-dependent vector corresponding to the selection environment. 7. The information processing apparatus of claim 5, wherein: each of elements of the preference vector of each selection subject and the environment-dependent vector in each selection environment is represented by a prior distribution; and the learning processing section calculates distribution parameters of the prior distribution on each of the elements of the preference vector of each selection subject and the environment-dependent vector in each selection environment by learning. 8. The information processing apparatus of claim 7, wherein the learning processing section is configured to: generate distribution parameters of prior distributions of the preference vector of each selection subject and the environment-dependent vector in each selection environment; generate a next sample of the environment-dependent vector in each selection environment based on the prior distribution of the environment-dependent vector in each selection environment; generate a next sample of the preference vector of each selection subject based on the prior distribution of the preference vector of each selection subject; and calculate distributions of the environment-dependent vector in each selection environment and the preference vector of each selection subject based on the samples of the environment-dependent vector in each selection environment and the preference vector of each selection subject that occur multiple times. 9. The information processing apparatus of claim 8, wherein the learning processing section is configured to: generate a sample candidate for the environment-dependent vector in each selection environment from a distribution based on the distribution parameters of the prior distribution of the environment-dependent vector and a previous sample of the environment-dependent vector; and determine whether to select each of sample candidates for the environment-dependent vector as a next sample of the environment-dependent vector based on an occurrence probability of the sample candidate for the prior distribution, and a likelihood of the sample candidate for selection in the history data and the preference vector of each selection subject. 10. The information processing apparatus of claim 8, wherein the learning processing section is configured to: generate a sample candidate for the preference vector of each selection subject from a distribution based on the distribution parameters of the prior distribution of the preference vector and a previous sample of the preference vector; and determine whether to select each of sample candidates for the preference vector of each selection subject as a next sample of the preference vector based on an occurrence probability of the sample candidate for the prior distribution, and a likelihood of the environment-dependent vector in each selection environment for selection in the history data and the sample candidate 11. The information processing apparatus of claim 5, wherein the learning processing section calculates the environment-dependent vector in each selection environment and the preference vector of each selection subject using an objective function based on a probability of selection in the history data in response to the environment-dependent vector in each selection environment and the preference vector of each selection subject. 12.-19. (canceled) 20. A nontransitory, computer readable storage medium having computer readable instructions stored thereon that, when executed by a computer, implement a method, comprising: acquiring a feature value for a feature possessed by each of a plurality of selection objects; acquiring history data including a history indicating that a plurality of selection subjects have selected selection objects; learning processing by allowing a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value for each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and outputting learning results obtained in the learning processing.