Abstract: An advertising effect prediction device is a device that predicts an advertising effect of advertising content, and includes: an acquisition unit that acquires advertisement information related to the advertising content; and a calculation unit that calculates the advertising effect based on the advertisement information. The advertisement information includes a plurality of images included in the advertising content, and the calculation unit calculates the advertising effect based on an arrangement order of the plurality of images in the advertising content.

Abstract: A recommendation device includes: a determination unit that determines a combination of insurance products and insurance premiums to be paid to the insurance products from among a plurality of insurance products based on a risk degree indicating a degree of damage caused to a user due to an event to be compensated for by each of the plurality of insurance products and a compensation degree indicating a degree of compensation by each of the plurality of insurance products; and an output unit that outputs recommendation information indicating the combination and the insurance premiums.

Abstract: An click rate prediction model construction device 1 includes: an image generating unit 13 configured to generate a plurality of similar images S similar to a basic image B which is displayed as an advertisement; a derivation unit 14 configured to derive an estimated value of a click rate of each of the plurality of similar images S on the basis of an actual value and a certainty factor of a click rate of the basic image B; and a model constructing unit 15 configured to learn the actual value and the estimated value of the click rate of the basic image B for each of the plurality of similar images S and to construct a click rate prediction model.

Abstract: A demand prediction device calculates time-series data of a prediction value of future demand for durable goods belonging to a particular broad division through time-series analysis, constructs a model for calculating a purchase probability based on attribute information, information on types of durable goods purchased by the users, and time information on the purchase, calculates purchase probability data including a purchase probability of the durable goods in each of time periods for each of users and a purchase probability for the broad division and each of the plurality of subdivisions of durable goods for each of the users by inputting the attribute information of the users to the model, and calculates and outputs a prediction value of the total demand for each of the plurality of subdivisions in a particular time period based on the time-series data of the prediction value and the purchase probability data.

Abstract: A price prediction device includes an attribute acquisition unit that acquires data indicating attributes of consumers, a history acquisition unit that acquires data indicating a history of a product purchased by the consumer and a purchase price of the product, a probability acquisition unit that derives a selection probability of a target product being selected from a product group for each consumer based on the attribute and the history, and a derivation unit that derives an optimal price of the target product for each consumer based on the selection probability of the target product and a selling price of the target product, by using a price prediction model constructed by machine learning.

Abstract: An inventory management device (1) includes an acquisition unit (11) that acquires a first learned model related to inventory management of old products, and relevance information related to relevance between the old products and new products, an assessment unit (14) that assesses whether or not the first learned model is applied to inventory management of the new products, based on the relevance information, and a determination unit (15) that applies the first learned model to the inventory management of the new products when the assessment unit (14) assesses that the first learned model is applied, and determines a policy for the inventory management of the new products.

Abstract: An advertisement budget optimization device (1) includes an estimation unit (13) that estimates a cost per click of an advertisement for each predetermined unit period (specifically, one day) included in a delivery period of an advertisement campaign as a delivery target, and a determination unit (14) that determines an advertisement budget for each unit period so that a total number of clicks of the advertisement is maximized, based on the cost per click in each unit period estimated by the estimation unit (13) and a total budget for the advertisement campaign in the delivery period.

Abstract: The at-home prediction device is a device that makes a prediction relating to a resident in a household being at home, and the at-home prediction device includes: a household information acquiring unit configured to acquire household information according to the number of households of each type in an area in which the household are located; a prediction information acquiring unit configured to acquire prediction information of a plurality of types other than the household information in the area that is used for the prediction; a dimension compressing unit configured to perform dimension compression on the prediction information together with the household information for each type of the prediction information; a prediction unit configured to make a prediction relating to the resident in the household being at home on the basis of information that is compressed in dimensions; and an output unit configured to output information representing a prediction result.

Abstract: The at-home prediction device is a device that makes a prediction relating to a resident in a household being at home, and the at-home prediction device includes: a household information acquiring unit configured to acquire household information according to the number of households of each type in an area in which the household are located; a prediction information acquiring unit configured to acquire prediction information of a plurality of types other than the household information in the area that is used for the prediction; a dimension compressing unit configured to perform dimension compression on the prediction information together with the household information for each type of the prediction information; a prediction unit configured to make a prediction relating to the resident in the household being at home on the basis of information that is compressed in dimensions; and an output unit configured to output information representing a prediction result.

Abstract: The visit-time-determining device is a device that determines a visit time for a plurality of visit destinations, and the visit-time-determining device includes a visit time determination unit configured to determine a visit time for each of the visit destinations, a clustering unit configured to cluster visit destinations on the basis of geographical positions of the visit destinations at each of the determined visit times, and a re-determination visit destination selection unit configured to select a visit destination for which the visit time is to be re-determined from the plurality of visit destinations on the basis of a result of the clustering, wherein the visit time determination unit re-determines the visit time of the visit destination selected by the re-determination visit destination selection unit from times other than determined visit time.