Abstract: An induction heating system includes induction heating apparatuses, each including a high-frequency current transformer, a low-frequency current transformer and a heating coil, a high-frequency input switch connected to the high-frequency current transformer, a low-frequency input switch connected to the low-frequency current transformer, a first power source to output a high-frequency electric power and a low frequency electric power, a second power source, a first power source output switch connectable to the first power source, a second power source output switch connectable to the second power source, and a switch controller. Each induction heating apparatus includes a heater controller to send a signal to the switching controller to turn on one of the first power source output switch and the second power source output switch, to turn off the other, and to switch on or off each of the high-frequency input switch and the low-frequency input switch.

Abstract: A recommendation device includes an acquisition unit acquiring a visual line image obtained by imaging the trajectory of the visual line of a user browsing a content displayed on a user terminal, an extraction unit extracting a feature vector indicating a feature amount of the visual line image, a generation unit generating a correspondence relationship between the content and the trajectory of the visual line of the user as a user context on the basis of the feature vector, a computation unit computing the score of each of a plurality of contents, which are recommendation candidates, by using the user context, and an output unit outputting a recommendation result of a content selected on the basis of the score.

Abstract: A recommendation device includes a storage unit storing a content feature vector and a user feature vector, an acquisition unit acquiring information indicating a plurality of favorite contents and preference information indicating the preference of a user, a learning unit performing learning such that the position of the user feature vector and the position of the content feature vector of the plurality of favorite contents approach each other, a correction unit correcting the position of the user feature vector by using weighting based on the preference information, a computation unit computing a score of each of the plurality of contents on the basis of a separation distance between the position of the user feature vector and the position of the content feature vector, and an output unit outputting a recommendation result of a content selected on the basis of the score.

Abstract: A recommendation system includes: a first acquisition unit configured to acquire user history information; a calculation unit configured to calculate a use maturity representing maturity of use of a service for each of a plurality of users on the basis of the user history information; a second acquisition unit configured to acquire recommendation history information including target algorithm information and recommendation success/non-success information; a selection unit configured to select a target algorithm on the basis of the recommendation history information of a user group having a use maturity corresponding to the use maturity of the target user among the plurality of users; and a recommendation unit configured to determine a content to be recommended to the target user using the selected target algorithm and output information representing the determined content.

Abstract: The server 10 includes: a display control unit 14 that displays link image IM in top screen SC1; a first model generator 161 that generates a first model M1 configured to output, for each content, a first score related to a probability that the link image IM is selected by a user by machine learning based on information indicating whether link image IM is selected by a user, a user attribute of the user, and information indicating content displayed as link image IM; a user attribute acquisition unit 12 that acquires a user attribute of a target user; and an image determining unit 131 that preferentially determines an image of content having a high first score as an image associated with link image IM in top screen SC1 presented to the target user.

Abstract: The information processing device 30 includes: an extraction unit 33 for extracting a target user who has used content in a content delivery service providing the content to a user; an acquisition unit 34 for acquiring preference information related to a preference of the target user extracted by the extraction unit 33 and including a plurality of first item values related to a predetermined preference; and a tendency information generation unit 35 for generating tendency information indicating a tendency of the content based on the preference information of the target user acquired by the acquisition unit 34.

Abstract: A recommendation system includes: a first acquisition unit configured to acquire user history information; a calculation unit configured to calculate a use maturity representing maturity of use of a service for each of a plurality of users on the basis of the user history information; a second acquisition unit configured to acquire recommendation history information including target algorithm information and recommendation success/non-success information; a selection unit configured to select a target algorithm on the basis of the recommendation history information of a user group having a use maturity corresponding to the use maturity of the target user among the plurality of users; and a recommendation unit configured to determine a content to be recommended to the target user using the selected target algorithm and output information representing the determined content.