RANKING FUNCTION GENERATING APPARATUS, RANKING FUNCTION GENERATING METHOD AND PROGRAM

1 . A ranking function generating apparatus comprising:

a memory; and

a processor configured to execute:

producing training data including at least a first search log related to a first item included in a search result of a search query, a second search log related to a second item included in the search result, and respective domains of the first search log and the second search log; and

learning, using the training data, parameters of a neural network that implements ranking functions for a plurality of domains through multi-task learning regarding each of the domains as a task.

2 . The ranking function generating apparatus according to claim 1 , wherein

the neural network includes a plurality of output layers that output scholar values representing ranks of items in the plurality of individual domains, and

the learning learns the parameters so as to minimize a value of a loss function defined using a difference between a first output value from the neural network for the domains included in the training data and the first item and a second output value from the neural network for the domains and the second item and using the first search log and the second search log.

3 . The ranking function generating apparatus according to claim 2 , wherein the training data includes a feature value of the first item and a feature value of the second item,

wherein the first output value is an output value from the output layer corresponding to the domains included in the training data, among a plurality of output values output by inputting the feature value of the first item to the neural network, and

wherein the second output value is an output value from the output layer corresponding to the domains included in the training data, among a plurality of output values output by inputting the feature value of the second item to the neural network.

4 . The ranking function generating apparatus according to claim 2 , wherein the learning calculates, from the difference, a probability that the first item is ranked higher than the second item in one of the domains and calculates a value determined from the first search log and the second search log to calculate a value of the loss function and a gradient of the loss function related to the parameters, and

wherein the learning uses the value of the loss function and the gradient of the loss function related to the parameters to learn the parameters.

5 . The ranking function generating apparatus according to claim 1 , wherein each of the search logs is information representing the number of times a user behavior of a predetermined type was performed with respect to the item included in the search result of the search query, and

wherein the domain is the type of the user behavior corresponding to the search log.

6 . A ranking function generating method, executed by a computer including a memory and a processor, the method comprising:

producing training data including at least a first search log related to a first item included in a search result of a search query, a second search log related to a second item included in the search result, and respective domains of the first search log and the second search log; and

learning, using the training data, parameters of a neural network that implements ranking functions for a plurality of domains through multi-task learning regarding each of the domains as a task.

7 . A non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer to function as the ranking function generating apparatus according to claim 1 .