Kabushiki Kaisha Toshiba

Tomoko Murakami

Ryohei Orihara

Shigeaki Sakurai

Akihiro Suyama

Hideki Kobayashi

Kouichirou Mori

Takashi Koiso

Naoki Imasaki

According to one embodiment, profile information of new user and items to be selected are inputted. Each item has an attribute value of a plurality of attributes. Profile information and preference information of a plurality of users are acquired. The preference information represents whether each user has selected each item. The plurality of users is classified into a plurality of clusters by the profile information and the preference information of the plurality of users. A parameter of each attribute of each cluster is calculated by the preference information of each cluster. A similar cluster to classify the new user is estimated from the plurality of clusters by the profile information of the new user. A preference degree of each item is calculated by the parameter of each attribute of the similar cluster and the attribute value of each item. An item to be recommended is decided by the preference degree.

Apparatus and method for recommending information, and non-transitory computer readable medium thereof

Described herein is an apparatus that provides recommended program information based on a viewing history of an audience. The apparatus includes an extractor to extract, based on a specific performer whom a viewing audience likes, a similar performer. The extractor extracts the similar performer using a relation between a plurality of performers. The extractor selects a node satisfying a first condition that a number of links between a first node corresponding to the specific performer and one or more second nodes corresponding to the similar performer is not more than a predetermined upper limit and a second condition that the number of links between the first node and second nodes is not more than a threshold from the second nodes. The apparatus also includes a selector to select a recommended program from a plurality of broadcast planned programs, and a creator to create the recommended program information.

Apparatus and method of providing a recommended broadcast program

A viewing behavior learning apparatus includes: a viewing history acquiring unit that acquires a viewing record that indicates an attribute of a program and start and end times during which a viewer viewed the program; a viewing history dividing unit that divides the viewing record every discretized time points that is discretized by a unit time; a viewing history replicating unit that discretizes a viewing time period denoted by the divided viewing records by interval shorter than the unit time to obtain discretized viewing time periods; a viewing behavior storage unit that builds a model of the viewing behavior by use of a Bayesian network having the viewing time period and the attribute as random variables and stores a conditional probability table of the Bayesian network; and a viewing behavior updating unit that updates the conditional probability table using the discretized viewing time periods.

Viewing behavior learning apparatus

A content distribution device includes a first receiver which receives, from the broadcasting station, the program content having the commercial image data and electronic program information; an commercial extractor which extracts the commercial image data and creates commercial metadata based on the electronic program information; a first storage which stores the commercial image data and the commercial metadata that are associated with each other; a second storage which stores distribution contents; a content selector which selects the distribution content according to a request from the view terminal from the second storage; a third receiver which receives, from the viewer terminal, viewing history information of program contents; an commercial inserter which selects the commercial metadata from the first storage based on the viewing history information and inserts the commercial image data corresponding to the selected commercial metadata in the distribution content; and a distribution unit which distributes the distribution content to the viewer terminal.

Device and terminal for inserting commercial image data in distribution content and method thereof

A system includes: a preference model generating unit that generates a preference model by learning and storing the preference model for a user based on a behavior history that indicates history of behavior of the user; an evaluation list generating unit that calculates probability of a plurality of recommended candidates based on the preference model and generates an evaluation list indicating the probability of the recommended candidates; a default predicting unit that specifies habituation of the user based on the information on the behavior history and calculates a predicted default value of the plurality of recommended candidates based on the habituation; and an evaluated unexpectedness value calculating unit that calculates an evaluated unexpectedness value indicating unexpectedness where the unexpectedness is higher as a difference obtained by subtracting the predicted default value from the probability is larger.

System and method for evaluating information

A behavior prediction apparatus includes an input unit inputting sole behaviors of a human and simultaneous occurrence probability of behaviors, a behavior discrimination unit specifying a behavior pattern based on correspondence between behaviors corresponding to simultaneous occurrence probability and actual behaviors, an information generation/recording unit generating behavior history information of the specified behavior pattern, and to generate and record information of each of a behavior transition probability, a behavior time and a behavior occurrence probability based on the behavior history information, a behavior prediction unit obtaining a starting behavior from the behavior occurrence probability, select another behavior pattern in order of behavior occurrence probability, and add the behavior time to the selected behavior pattern to output behavior prediction information, and a social value calculation unit obtaining a social value per unit time using the behavior prediction information, social value unit information and the prediction period.

Behavior prediction apparatus and method therefor

The present invention provides an analysis model generating method for generating an analysis model expressed using a Bayesian network. A analysis model generating method includes selecting a child variable to be added to the analysis model; selecting a plurality of variable candidates to be added to the analysis model as parent variables in causal relationships with the child variable; judging whether each of values indicating the causal relationships between the selected variable candidates and the child variable is high or low, based on the causal relationships between the variable candidates and the child variable; generating an aggregated variable different from the variable candidates by aggregating a plurality of low causal variable candidates; and putting high causal variable candidate and the aggregated variable into the analysis model as the parent variables to the child variable.

Method, program product, and apparatus for generating analysis model

A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Growing community of inventors

Yokohama, Japan

Tomoko Murakami