Abstract: An information presentation device generates a plurality of training models by executing machine learning that uses training data. The information presentation device generates hierarchical information that represents, in a hierarchical structure, a relationship between hypotheses shared as common and hypotheses regarded as differences for a plurality of hypotheses extracted from each of the plurality of training models and each designated by a combination of one or more explanatory variables.

Abstract: A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing including: extracting, for a plurality of pieces of combination data each of which is a combination of a plurality of feature amounts that includes an invariable feature amount and a variable feature amount that represent features of a target, combination data to be processed based on the plurality of pieces of combination data according to relation between the respective pieces of combination data; executing causal search processing for the variable feature amount according to the invariable feature amount by using the combination data to be processed; and selecting, based on a result of the causal search processing, a specific variable feature amount that corresponds to the specified invariable feature amount, to present the selected specific variable feature.

Abstract: A non-transitory computer-readable recording medium stores therein an allocation program that causes a computer to execute a process including: performing, by using a part of data including an objective variable and one or more explanatory variables corresponding to the objective variable as training data, training of a model that predicts the objective variable from the explanatory variables of the data; classifying test data obtained by excluding the training data from the data into a group according to a classification condition regarding at least a part of the explanatory variables of the data; predicting the objective variable from the explanatory variables of the test data using the trained model for each of groups by which classification has been performed at the classifying; and calculating a predetermined resource amount to be allocated to each of the groups based on the objective variable for each of the groups predicted at the predicting.

Abstract: A non-transitory computer-readable recording medium stores therein an information processing program that causes a computer to execute a process including, deciding on one or more variables, from among a plurality of variables, to be a target for a question regarding degree of importance, based on order of priority of the variables and estimated amount, the order of priority of the variables being determined based on a plurality of patterns indicating ranking of the plurality of variables, the estimated amount indicating possibility of a match with predetermined condition regarding each of the patterns, and updating the estimated amount based on an answer result of the question about the decided variable.

Abstract: An information processing device classifies a plurality of linear models, each of which includes one or more variables, into a plurality of groups in such a way that the linear models which include identical variables included in each of the plurality of linear models and which have identical coefficient encoding with respect to the variables are grouped in the same group, outputs a first question used in deciding degree of importance of each explanatory variable included in training data which is used in training of the plurality of linear models, and, decides on an explanatory variable about which a second question is to be asked, when a linear model in which the degree of importance is reflected is to be selected from the plurality of linear models, based on extent of decrease in number of target groups for selection according to an answer to the first question.

Abstract: A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including obtaining a machine learning model trained by using training data, the machine learning model predicting presence or absence of purchase actions or predetermined actions of users corresponding to feature information on the users, the training data including feature information on users and information indicating the presence or the absence of the purchase actions of commercial products or the predetermined actions, receiving input of a budget amount in an entry field displayed on a display in association with a user group including users including a common feature in the feature information, and displaying, in association with the budget amount on the display, feature information on the users included in the user group corresponding to the entry field.

Abstract: A non-transitory computer-readable storage medium storing a prediction program for causing a computer to perform processing including: listing combinations of feature amounts that are correlated with a target label; creating a policy to achieve the target label for a prediction target based on a difference between the listed combinations of the feature amounts and a combination of feature amounts of the prediction target; and determining appropriateness of the created policy based on performance information that indicates past performances.

Abstract: A non-transitory computer-readable recording medium stores therein an extraction program that causes a computer to execute a process including: generating a plurality of combinations of conditions relating to a plurality of item values included in data; calculating an index value that indicates a degree of cooccurrence between a specified response variable and each of the plurality of combinations, by using a machine learning model that estimates a response variable from the plurality of item values, the machine learning model having been trained by using the data; and extracting a specific combination from among the plurality of combinations based on any one of the condition and the index value.

Abstract: A recording medium records a simulation program for causing a computer to execute a process including: arranging an agent, which has cognitive information and behaves in a virtual space based on the cognitive information, in the virtual space in which a plurality of guidance displays is set; when, with respect to each of the plurality of guidance displays, the agent enters within a first range of the respective guidance displays, and first guidance information corresponding to the first range is related with a destination of the agent, changing cognitive information up to the destination of the agent; and when, with respect to each of the plurality of guidance displays, the agent enters within a second range different from the first range, and second guidance information corresponding to the second range is related with the destination of the agent, changing cognitive information not directly related with the destination of the agent.

Abstract: A non-transitory computer-readable recording medium stores therein a predicting program that causes a computer to execute: receiving input data to be subjected to prediction; and generating, from training data sets each having explanatory variables and an objective variable, a prediction result, by using a hypothesis set and respective weights of hypotheses included in the hypothesis set, the hypotheses each being formed of a combination of the explanatory variables, classifying any of the training data sets and satisfying a specific condition, the weights being learnt based on whether each of the hypotheses holds true for each of the training data sets. The generating includes determining a value of a variable included in a pseudo-Boolean function such that a probability satisfies a predetermined standard, the probability being a probability that the prediction result satisfies the specific condition, the pseudo-Boolean function including variables corresponding to the explanatory variables.

Abstract: With respect to training data records in which combinations of data item values of data items are individually associated with label information, the data item values are converted based on a criterion per data item into discretized data values. Training processing for training a model that receives the discretized data values as input and performs determination about the label information is performed by using training data records obtained by the conversion. From an execution result of the training processing, feature data records, each of which differently indicates a combination of two or more data items for the determination among the data items, and index values, which indicate importance levels of the feature data records respectively, are acquired. The criterion for the discretization of the data item values is changed based on at least one of the feature data records having been selected based on the index values.

Abstract: A storage medium storing a pattern extraction program that causes a computer to execute a process includes acquiring sample set data associated with both of data item values related to each of a plurality of data items and label information regarding an event; acquiring a plurality of combination patterns, each of which is a combination of the data item values; determining evaluation values for each of the plurality of combination patterns based on a number of samples that satisfy each of the plurality of combination patterns among the samples indicated by the sample set data and a ratio of samples whose label information indicates a certain value to samples that satisfy each of the plurality of combination patterns; and extracting a combination pattern that corresponds to one of the evaluation values that has a local maximum value in the evaluation values from the plurality of combination patterns.

Abstract: An item presentation method implemented by a computer, the item presentation method includes: executing a selection processing that includes selecting a plurality of candidate items that are presentation candidates from a plurality of items, based on a preference of a user; executing a calculation processing that includes calculating the number of new items newly presented among the plurality of candidate items by referring to an item presentation history to the user; executing a decision processing when the number of new items is a predetermined number or less, the decision processing including deciding, as a recommendation item, an item located away from the preference in an item space about the preference by a distance less than a threshold value; and executing a presentation processing that includes selecting a presentation item presented to the user from the plurality of candidate items and the recommendation item, and presenting the selected presentation item.

Abstract: A simulation method is performed by a computer by using an agent disposed in a virtual space. The method includes: controlling the agent so that the agent behaves in the virtual space under influence of each of a plurality of signs disposed in the virtual space; and determining the influence of each of the plurality of signs on the agent in accordance with an attribute relating to a display mode of the sign and an attribute of the agent.

Abstract: A search device, for each of a plurality of inquiries including a plurality of options, based on a log of answers of a respondent in the past, specifies a matching option corresponding to a trend of answers of the respondent from the plurality of options to be represented to the respondent. The search device, when an answer to the plurality of options is included in the matching option, determines that it is a preference-reached mode that is a mode where the estimating a preference of the user converges. The search device transmits, to the respondent, an inquiry including options, contents of the options included in the inquiry being changed depend on whether it is determined to be a preference-reached mode in the determining.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including: inputting, to each of a plurality of agents, a plurality of purchasing behavioral objectives, a first purchasing behavioral objective, and facilities information indicating a plurality of facilities corresponding to the plurality of purchasing behavioral objectives; and causing each of the plurality of agents to determine, based on a condition that has been set in accordance with a facility search behavior with respect to a facility that is selected from the plurality of facilities in response to the purchasing behavioral objective of each of the plurality of agents at a time of execution of simulation, a second purchasing behavioral objective of each of the plurality of agents from among the plurality of purchasing behavioral objectives.

Abstract: A non-transitory computer-readable recording medium stores therein a predicting program that causes a computer to execute: receiving input data to be subjected to prediction; and generating, from training data sets each having explanatory variables and an objective variable, a prediction result, by using a hypothesis set and respective weights of hypotheses included in the hypothesis set, the hypotheses each being formed of a combination of the explanatory variables, classifying any of the training data sets and satisfying a specific condition, the weights being learnt based on whether each of the hypotheses holds true for each of the training data sets. The generating includes determining a value of a variable included in a pseudo-Boolean function such that a probability satisfies a predetermined standard, the probability being a probability that the prediction result satisfies the specific condition, the pseudo-Boolean function including variables corresponding to the explanatory variables.

Abstract: A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including receiving input data to be predicted, generating a tree structure data based on a plurality of pieces of rule information each indicated by an association of a combination of attribute values of a plurality of attributes with a label according to a predetermined order condition for the plurality of attributes, the tree structure data being obtained by aggregating the plurality of pieces of rule information, the tree structure data including an attribute value as a branch, and determining a degree of contribution to make a determination result on a predetermined value of a predetermined attribute reach a predetermined label based on a likelihood of obtaining a value of the predetermined label as the determination result, when the attribute value of the predetermined attribute is determined to be the predetermined value.

Abstract: A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including obtaining a machine learning model trained by using training data, the machine learning model predicting presence or absence of purchase actions or predetermined actions of users corresponding to feature information on the users, the training data including feature information on users and information indicating the presence or the absence of the purchase actions of commercial products or the predetermined actions, receiving input of a budget amount in an entry field displayed on a display in association with a user group including users including a common feature in the feature information, and displaying, in association with the budget amount on the display, feature information on the users included in the user group corresponding to the entry field.

Abstract: An item presentation method implemented by a computer, the item presentation method includes: executing a selection processing that includes selecting a plurality of candidate items that are presentation candidates from a plurality of items, based on a preference of a user; executing a calculation processing that includes calculating the number of new items newly presented among the plurality of candidate items by referring to an item presentation history to the user; executing a decision processing when the number of new items is a predetermined number or less, the decision processing including deciding, as a recommendation item, an item located away from the preference in an item space about the preference by a distance less than a threshold value; and executing a presentation processing that includes selecting a presentation item presented to the user from the plurality of candidate items and the recommendation item, and presenting the selected presentation item.