A processing apparatus is disclosed for representing cognitively biased selection behavior of a consumer as a learnable model with high prediction accuracy taking into account even feature values of a product and the consumer. The processing apparatus generates a selection model obtained by modeling selection behavior of a selection entity that selects at least one choice out of presented input choices. The processing apparatus includes an acquiring unit to acquire training data including a plurality of input feature vectors that indicate features of a plurality of the choices presented to the selection entity and an output feature vector that indicates a feature of an output choice. The processing apparatus further includes an input combining unit to combine the plurality of input vectors to generate an input combined vector, and a learning processing unit to learn a selection model on the basis of the input combined vector and the output vector.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing system comprising: memory; a processor communicatively coupled to the memory; and a processing apparatus communicatively coupled to the memory and the processor, wherein the processing apparatus generates a selection model obtained by modeling selection behavior of a selection entity that selects at least one choice out of presented input choices, the processing apparatus comprising: an acquiring unit configured to acquire training data including a plurality of input feature vectors that indicate features of a plurality of the input choices presented to the selection entity and an output feature vector that indicates a feature of an output choice, which is a choice selected by the selection entity out of the plurality of input choices; an input combining unit configured to combine the plurality of input feature vectors to generate an input combined vector; and a learning processing unit configured to learn a selection model on the basis of the input combined vector and the output feature vector. 2. The information processing system according to claim 1, wherein the processing apparatus further comprises: an output combining unit configured to combine a plurality of the output feature vectors concerning a plurality of the output choices selected by the selection entity out of the plurality of input choices to generate an output combined vector, wherein the learning processing unit learns the selection model on the basis of the input combined vector and the output combined vector. 3. The information processing system according to claim 2, wherein the selection model comprises a plurality of input nodes corresponding to a plurality of input feature values included in the input combined vector, a plurality of output nodes corresponding to a plurality of output feature values included in the output combined vector, and a plurality of intermediate nodes, and the learning processing unit learns a plurality of weight parameters provided between the plurality of input nodes and the plurality of intermediate nodes and between the plurality of output nodes and the plurality of intermediate nodes. 4. The information processing system according to claim 2, wherein each of the plurality of input feature vectors and the plurality of output feature vectors is a binary vector. 5. The information processing system according to claim 4, wherein the acquiring unit comprises a binarizing unit configured to binarize feature parameters of choices of each of the plurality of input choices and each of the plurality of output choices and generate a feature vector of each of the plurality of input feature vectors and each of the plurality of output feature vectors. 6. The information processing system according to claim 5, wherein the binarizing unit converts feature parameters closer to each other into feature vectors closer to each other. 7. The information processing system according to claim 2, wherein the input combining unit generates, for the plurality of input choices, the input combined vector that takes the same value without depending on order of the input choices, and the output combining unit generates, for the plurality of output choices, the output combined vector that takes the same value without depending on order of the output choices. 8. The information processing system according to claim 7, wherein the input combining unit and the output combining unit generate the input combined vector and the output combined vector based on an average of the plurality of input feature vectors and an average of the plurality of output feature vectors. 9. The information processing system according to claim 7, wherein the acquiring unit acquires the plurality of input feature vectors and the plurality of output feature vectors, which are binary vectors, and the input combining unit and the output combining unit generate the input combined vector and the output combined vector based on a logical operation of the plurality of input feature vectors and a logical operation of the plurality of output feature vectors. 10. The information processing system according to claim 9, wherein the input combining unit and the output combining unit generate the input combined vector and the output combined vector based on logical OR of each of elements of the plurality of input feature vectors and logical OR of each of elements of the plurality of output feature vectors. 11. The information processing system according to claim 1, wherein in response to selection behavior of the selection entity being predicted, the acquiring unit acquires the plurality of input feature vectors corresponding to the plurality of input choices presented to the selection entity, the input combining unit combines the plurality of input feature vectors to generate the input combined vector, and the processing apparatus comprises: a selecting unit configured to select, out of the plurality of input feature vectors corresponding to the plurality of input choices, an output feature vector of the output choice, which is a choice, possibility of which being selected by the selection entity is estimated; and an estimating unit configured to estimate, on the basis of the input combined vector and the output feature vector, possibility of the output choice being selected in the selection model. 12. A method for generating a selection model obtained by modeling selection behavior of a selection entity that selects at least one choice out of presented input choices, the method comprising: acquiring training data comprising a plurality of input feature vectors that indicate features of a plurality of the input choices presented to the selection entity and an output feature vector that indicates a feature of an output choice, which is a choice selected by the selection entity out of the plurality of input choices; combining the plurality of input feature vectors to generate an input combined vector; and learning a selection model on the basis of the input combined vector and the output feature vector. 13. The method according to claim 12, further comprising: combining a plurality of the output feature vectors concerning a plurality of the output choices selected by the selection entity out of the plurality of input choices to generate an output combined vector, wherein the selection model is learned on the basis of the input combined vector and the output combined vector. 14. The method according to claim 13, wherein the selection model comprises a plurality of input nodes corresponding to a plurality of input feature values included in the input combined vector, a plurality of output nodes corresponding to a plurality of output feature values included in the output combined vector, and a plurality of intermediate nodes, and wherein the learning further comprises learning a plurality of weight parameters provided between the plurality of input nodes and the plurality of intermediate nodes and between the plurality of output nodes and the plurality of intermediate nodes. 15. The method according to claim 13, wherein each of the plurality of input feature vectors and the plurality of output feature vectors is a binary vector. 16. The method according to claim 15, further comprising: binarizing feature parameters of choices of each of the plurality of input choices and each of the plurality of output choices and generate a feature vector of each of the plurality of input feature vectors and each of the plurality of output feature vectors. 17. A computer program product for generating a selection model obtained by modeling selection behavior of a selection entity that selects at least one choice out of presented input choices, the computer program product comprising: a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising: acquiring training data comprising a plurality of input feature vectors that indicate features of a plurality of the input choices presented to the selection entity and an output feature vector that indicates a feature of an output choice, which is a choice selected by the selection entity out of the plurality of input choices; combining the plurality of input feature vectors to generate an input combined vector; and learning a selection model on the basis of the input combined vector and the output feature vector. 18. The computer program product according to claim 17, wherein the method further comprises: combining a plurality of the output feature vectors concerning a plurality of the output choices selected by the selection entity out of the plurality of input choices to generate an output combined vector, wherein the selection model is learned on the basis of the input combined vector and the output combined vector. 19. The computer program product according to claim 18, wherein the selection model comprises a plurality of input nodes corresponding to a plurality of input feature values included in the input combined vector, a plurality of output nodes corresponding to a plurality of output feature values included in the output combined vector, and a plurality of intermediate nodes, and wherein the learning further comprises learning a plurality of weight parameters provided between the plurality of input nodes and the plurality of intermediate nodes and between the plurality of output nodes and the plurality of intermediate nodes. 20. The computer program product according to claim 18, wherein each of the plurality of input feature vectors and the plurality of output feature vectors is a binary vector.