Abstract: An information processing apparatus includes: a display section provided with a screen capable of displaying a content of an application; a user-input processing section for acquiring an instruction input from a user; and a screen-hierarchy control section for managing a screen hierarchical order of each application when contents of a plurality of started applications are displayed in the display section. When one of at least two applications of a plurality out of the applications is set to be a master application and the other of the applications is set to be a slave application, the screen-hierarchy control section disposes and links the slave application at an immediately lower level than that of the master application in the screen hierarchy, and performs screen-hierarchy control such that the master application and the slave application are moved in the screen hierarchy in a state being linked at the time of updating the screen hierarchy.

Abstract: A method and system for generating a search result for a query of hierarchically organized documents based on retrieval of subtrees that are key resources for topic distillation is provided. The retrieval system may identify documents relevant to a query using conventional searching techniques. The retrieval system then calculates a subtree feature for subtrees that have an identified document as their root. After the retrieval system calculates the subtree feature for the subtrees, the retrieval system may generate a subtree relevance score for each subtree based on its subtree feature. The retrieval system may then order the identified documents based on their corresponding subtree relevances.

Abstract: A method and system for ranking importance of vertices of a directed graph based on links between the vertices and some prior knowledge of importance of the vertices is provided. A ranking system inputs an indication of the vertices along with an indication of the links between the vertices as the directed graph. The ranking system generates a transition-probability matrix that represents the probability of transitioning from vertex to vertex. The ranking system then generates a ranking of the vertices based on the links between the vertices represented by the stationary distribution of the transition-probability matrix that is minimally perturbed to satisfy the prior knowledge, which may be a partial ranking of the vertices.

Abstract: A method and system for generating a search result for a query of hierarchically organized documents based on retrieval of subtrees that are key resources for topic distillation is provided. The retrieval system may identify documents relevant to a query using conventional searching techniques. The retrieval system then calculates a subtree feature for subtrees that have an identified document as their root. After the retrieval system calculates the subtree feature for the subtrees, the retrieval system may generate a subtree relevance score for each subtree based on its subtree feature. The retrieval system may then order the identified documents based on their corresponding subtree relevances.

Abstract: This disclosure describes various exemplary methods, computer program products, and systems for selecting features for ranking in information retrieval. This disclosure describes calculating importance scores for features, measuring similarity scores between two features, selecting features that maximizes total importance scores of the features and minimizes total similarity scores between the features. Also, the disclosure includes selecting features for ranking that solves an optimization problem. Thus, this disclosure identifies relevant features by removing noisy and redundant features and speeds up a process of model training.

Abstract: Procedures for learning and ranking items in a listwise manner are discussed. A listwise methodology may consider a ranked list, of individual items, as a specific permutation of the items being ranked. In implementations, a listwise loss function may be used in ranking items. A listwise loss function may be a metric which reflects the departure or disorder from an exemplary ranking for one or more sample listwise rankings used in learning. In this manner, the loss function may approximate the exemplary ranking for the plurality of items being ranked.

Abstract: A method and system for ranking importance of vertices of a directed graph based on links between the vertices and some prior knowledge of importance of the vertices is provided. A ranking system inputs an indication of the vertices along with an indication of the links between the vertices as the directed graph. The ranking system generates a transition-probability matrix that represents the probability of transitioning from vertex to vertex. The ranking system then generates a ranking of the vertices based on the links between the vertices represented by the stationary distribution of the transition-probability matrix that is minimally perturbed to satisfy the prior knowledge, which may be a partial ranking of the vertices.

Abstract: An information processing apparatus includes: a display section provided with a screen capable of displaying a content of an application; a user-input processing section for acquiring an instruction input from a user; and a screen-hierarchy control section for managing a screen hierarchical order of each application when contents of a plurality of started applications are displayed in the display section. When one of at least two applications of a plurality out of the applications is set to be a master application and the other of the applications is set to be a slave application, the screen-hierarchy control section disposes and links the slave application at an immediately lower level than that of the master application in the screen hierarchy, and performs screen-hierarchy control such that the master application and the slave application are moved in the screen hierarchy in a state being linked at the time of updating the screen hierarchy.

Abstract: A method and system for learning a ranking function that uses a normalized, query-level error function is provided. A ranking system learns a ranking function using training data that includes, for each query, the corresponding documents and, for each document, its relevance to the corresponding query. The ranking system uses an error calculation algorithm that calculates an error between the actual relevances and the calculated relevances for the documents of each query. The ranking system normalizes the errors so that the total errors for each query will be weighted equally. The ranking system then uses the normalized error to learn a ranking function that works well for both queries with many documents in their search results and queries with few documents in their search results.

Abstract: A method and system for projecting queries and images into a similarity space where queries are close to their relevant images is provided. A similarity space projection (“SSP”) system learns a query projection function and an image projection function based on training data. The query projection function projects the relevance of the most relevant words of a query into a similarity space and the image projection function projects the relevance to an image of the most relevant words of a query into the same similarity space so that queries and their relevant images are close in the similarity space. The SSP system can then identify images that are relevant to a target query and queries that are relevant to a target image using the projection functions.

Abstract: A method and system for generating a search result for a query of hierarchically organized documents based on retrieval of subtrees that are key resources for topic distillation is provided. The retrieval system may identify documents relevant to a query using conventional searching techniques. The retrieval system then calculates a subtree feature for subtrees that have an identified document as their root. After the retrieval system calculates the subtree feature for the subtrees, the retrieval system may generate a subtree relevance score for each subtree based on its subtree feature. The retrieval system may then order the identified documents based on their corresponding subtree relevances.