Yahoo Japan Corporation

International Business Machines Corporation

Akira Tajima

Koji Tsukamoto

Hiroyuki Kato

Kazuhiko Shimura

Takenobu Dei

Akihiro Tanabe

Toshihiro Takahashi

Tsuyoshi Ide

Shoko Suzuki

Yuta Tsuboi

Hisashi Kashima

Shohei Hido

Rikiya Takahashi

Shinichiro Sega

Toru Takata

Rin Katou

Nobuyuki Akashi

Nari Woo

Hiroshi Nishikawa

Hidehito Gomi

Jonathan R Hosking

Kazuki Nakayama

Grace Y Lin

Kiyoshi Sasaki

Takahiro Ishikawa

Roger R Gung

Taisuke Fujimoto

Manabu Miyasaka

Masatsugu Shidachi

Seira Nakamura

An information processing device according to the present application includes a generation unit and a providing unit. The generation unit uses a model that is trained to learn a relationship between a criterion for classifying users of a first company and a criterion for classifying users of a second company to generate a criterion (common key) for classifying the users of the second company into a first category, from the criterion for classifying the users of the first company into the first category. The providing unit provides a criterion generated by the generation unit.

Information processing device, information processing method, and non-transitory computer readable storage medium

An estimation device according to the present application includes an acquiring unit and an estimation unit. The acquiring unit acquires user relationship information indicating a similarity relationship between a plurality of users. The estimation unit estimates, based on feeling information on a similar user who is similar to one of the users in the user relationship information acquired by the acquiring unit, a feeling of the one of the users.

Estimation device, estimation method, and non-transitory computer-readable recording medium

According to one aspect of an embodiment a provision device includes a generation unit that generates a distributed representation of each context on the basis of a relative connection that multiple contexts have. The provision device includes a provision unit that provides information representing a change between a distributed representation of a given context that is generated before distribution information about the given context is distributed and a distributed representation of the given context that is generated after the distribution information is distributed.

Provision device, provision method and non-transitory computer readable storage medium

An auction apparatus according to the application includes a plan receiving unit and an auction providing unit. The plan receiving unit receives plan information indicating a plan of content from a content creator who creates the content. The auction providing unit presents the plan information received by the plan receiving unit and provides an auction service in which bid targets are a right to display an advertisement in an advertising space that is set to the content and a right to replace the advertisement displayed in the advertising space.

Auction apparatus and auction method

An advertisement choosing device chooses an advertisement to be displayed in a display space of a predetermined size. The advertisement choosing device includes an advertisement storing unit and an advertisement choosing unit. The advertisement storing unit stores therein sizes of multiple advertisements of various sizes. The advertisement choosing unit chooses, from among the multiple advertisements, one or more advertisements whose total size is equal to or less than the size of the display space as the advertisement to be displayed in the display space.

Advertisement choosing device and advertisement choosing method

An advertisement distribution apparatus according to an embodiment includes a user information table, a determining unit, a request unit, and a distribution unit. The user information table stores user information about a user. When the advertisement distribution apparatus is accessed by a user terminal, the determining unit determines whether the user information of the user of the user terminal has been stored in the user information table. When the determining unit determines that the user information has not been stored in the storage unit, the request unit requests an information provision apparatus to transmit the user information. When the determining unit determines that the user information has not been stored in the storage unit, the distribution unit distributes advertisement content to the user terminal using the user information which is acquired from the information provision apparatus by the request unit.

Advertisement distribution apparatus, distribution method, and distribution program

An auction apparatus according to the application includes a plan receiving unit and a providing unit. The plan receiving unit gradually receives plan information indicating a plan of content from a content creator who creates the content a plurality of times. The providing unit additionally presents the plan information which is gradually received by the plan receiving unit and provides an auction service in which a bid target is a right to display an advertisement in an advertising space set to the content.

An advertisement delivery management apparatus according to an embodiment includes an allocation unit and an advertisement delivery unit. The allocation unit selects delivery target users who have the user attribute designated by each of order requests and whose number corresponds to the number of deliveries designated by each of order requests, from a plurality of delivery target users predicted as future advertisement delivery destinations, and allocates the selected delivery target users to the order requests. The advertisement delivery unit selects the advertising information of the order request corresponding to a user attribute of a user of a user terminal on the basis of the allocation result of the allocation unit and transmits the selected advertising information to the user terminal.

Advertisement delivery management apparatus and advertisement delivery management method

An advertisement delivery management apparatus according to an embodiment of the present disclosure includes a prediction information storage unit, an acquisition unit and an allocation unit. The prediction information storage unit stores information including user attributes of a plurality of delivery target users predicted as future advertisement delivery destinations. The acquisition unit acquires a plurality of order requests designating user attributes and the numbers of deliveries. The allocation unit allocates the delivery target users to the plurality of order requests, on the basis of the user attributes and the numbers of deliveries designated by the plurality of order requests, such that the number of predetermined user attributes of the delivery target users who have not been allocated to the order requests is the maximum.

An advertisement delivery management apparatus according to an embodiment includes a predicted information storage unit, an allocating unit, and a reallocating unit. The predicted information storage unit stores information including user attributes of a plurality of delivery target users predicted as future advertisement delivery destinations. The allocating unit allocates the delivery target user to an advertisement delivery order request based on the information stored in the predicted information storage unit. The reallocating unit performs a reallocating process of exchanging the delivery target user allocated to the order request with a non-allocated user who is the delivery target user that has not been allocated to the order request.

An advertisement delivery management apparatus according to an embodiment includes an acquisition unit, a determination unit, and an accepting unit. The acquisition unit acquires information on two or more types of delivery target user groups, each including a plurality of delivery target users predicted as future advertisement delivery destinations. The determination unit determines whether each of the delivery target user groups satisfies delivery conditions designated by an advertisement delivery order request. The accepting unit accepts the advertisement delivery order request based on the determination result of the determination unit.

A network system includes an application service provider (ASP) which is connected to the Internet and executes an application, and a CPU resource provider which is connected to the Internet and provides a processing service to a particular computational part (e.g., computation intensive part) of the application, wherein: when requesting a job from the CPU resource provider, the application service provider (ASP) sends information about estimated computation time of the job to the CPU resource provider via the Internet; and the CPU resource provider assigns the job by correcting this estimated computation time based on the estimated computation time sent from the application service provider (ASP).

Providing computational resources to applications based on accuracy of estimated execution times provided with the request for application execution

Location estimation systems, methods, and non-transitory computer program products. The system includes: storage means provided in the computer, means for storing the vector datasets in the storage means of the computer, means for calculating the similarity between the vector dataset without any location label and each neighboring vector dataset with a location label, by using any one of a q-norm where 0&#x2266;q&#x2266;1 and an exponential attenuation function, and means for estimating the location label of the vector data without any location label from the calculated similarities.

Location estimation system, method and program

Providing resources to a job comparing estimated execution time on currently available nodes and estimated execution time on nodes available after completion of a currently executing job

A location estimation method using label propagation. The achieved location estimation method is robust to variations in radio signal strengths and is highly accurate by using the q-norm (0&#x3c;q&#x3c;1), especially, for calculating the similarities among radio signal strength vectors. The accuracy in location estimation is further improved by putting more importance on the time-series similarities. Specifically, the time-series similarity is calculated by using time-series values indicating the temporal order of radio signal strengths during the measurement. If the time-series similarity is larger than the similarity between the radio signal strength vectors, the time-series similarity is preferentially used. The exponential attenuation function can also be used for calculating the similarities, instead of the q norm (0&#x3c;q&#x3c;1).

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Tokyo, Japan

Akira Tajima