A vector generation unit derives a reference feature vector and a document feature vector. A feature quantity extraction unit performs a dimensionality reduction process on the reference feature vector and the document feature vector so as to set a dimensional value as a first feature quantity and derives a cosine similarity between the reference feature vector and the document feature vector as a second feature quantity. A grid division unit classifies documents into first partial regions obtained by dividing a feature quantity space of the first feature quantity, and classifies the documents into second partial regions obtained by dividing a range of the second feature quantity. A training data extraction unit selects, for each combination of a first partial region and a second partial region, a document classified in both the partial regions and sets documents selected with respect to all combinations as training data.

A vector generation unit derives a reference feature vector and a document feature vector. A feature quantity extraction unit performs a dimensionality reduction process on the reference feature vector and the document feature vector so as to set a dimensional value as a first feature quantity and derives a cosine similarity between the reference feature vector and the document feature vector as a second feature quantity. A grid division unit classifies documents into first partial regions obtained by dividing a feature quantity space of the first feature quantity, and classifies the documents into second partial regions obtained by dividing a range of the second feature quantity. A training data extraction unit selects, for each combination of a first partial region and a second partial region, a document classified in both the partial regions and sets documents selected with respect to all combinations as training data.

A message selection device according to an embodiment includes a message database that holds a plurality of messages in correspondence with each of various emotion of a communicator, a communicator information acquisition unit configured to acquire communicator information for estimating the emotion of the communicator, a receiver information acquisition unit that acquires receiver information for estimating the emotion of a receiver who is to receive a message from the communicator, and a message selection unit that estimates the emotion of the communicator based on the communicator information acquired by the communicator information acquisition unit, estimates the emotion of the receiver based on the receiver information acquired by the receiver information acquisition unit, and selects a message from among the messages held by the message database based on the estimated emotions.

Disclosed is a computer-implemented method for determining a score adjustment for a search document, comprising determining a first attractiveness model of a first document from one or more documents based on one or more user interactions associated with the first document; determining a second attractiveness model of a second document from one or more documents based on one or more user interactions associated with the second document; determining one or more pairwise comparisons of documents based on the first and second attractiveness models of the first and second documents; training an adjustment model based on the pairwise comparisons of documents; and inputting the search document into the adjustment model to determine the score adjustment.

Data on entities and how they are associated with other entities may be aggregated from multiple sources and reconciled. The aggregated data may be presented in a dashboard with a graphical user interface (GUI) that represents entities (e.g., nodes) and associations (e.g., edges) as distinguishable graphical elements that are individually selectable. Different nodes/edges may have distinct graphical representations that correspond with certain characteristics of the nodes/edges. The dashboard may include multiple dynamically-updated panes that may be populated with different information depending on a user's interaction with the GUI and/or depending on information received from various sources. A first entity's connection to or involvement in certain activities may be more readily understood by interactively examining not just the first entity's relationship with a second entity, but also the second entity's relationship with a third entity which is not directly related to the first entity.

Data on entities and how they are associated with other entities may be aggregated from multiple sources and reconciled. The aggregated data may be presented in a dashboard with a graphical user interface (GUI) that represents entities (e.g., nodes) and associations (e.g., edges) as distinguishable graphical elements that are individually selectable. Different nodes/edges may have distinct graphical representations that correspond with certain characteristics of the nodes/edges. The dashboard may include multiple dynamically-updated panes that may be populated with different information depending on a user's interaction with the GUI and/or depending on information received from various sources. A first entity's connection to or involvement in certain activities may be more readily understood by interactively examining not just the first entity's relationship with a second entity, but also the second entity's relationship with a third entity which is not directly related to the first entity.

Systems and methods are disclosed for providing content by generating a bookmark data structure for a topic based on determining retrieval of a first content item related to the topic, of a first content type. In response to determining retrieval of the first content item, the system may add the first content item to the bookmark data structure for the topic. The system may then determine retrieval of a second content item related to the topic, of a second content type and, in response to determining retrieval of the second content item, the system may add the second content item to the bookmark data structure for the topic. The system may generate, for display in a user interface (UI), a menu based on the data structure, with interactive UI elements that provide preview and/or access to the content item when interaction with the UI element is detected.

Systems and methods are disclosed for providing content by generating a bookmark data structure for a topic based on determining retrieval of a first content item related to the topic, of a first content type. In response to determining retrieval of the first content item, the system may add the first content item to the bookmark data structure for the topic. The system may then determine retrieval of a second content item related to the topic, of a second content type and, in response to determining retrieval of the second content item, the system may add the second content item to the bookmark data structure for the topic. The system may generate, for display in a user interface (UI), a menu based on the data structure, with interactive UI elements that provide preview and/or access to the content item when interaction with the UI element is detected.

Methods, systems, and media for identifying abusive user accounts based on playlists are provided. In accordance with some embodiments of the disclosed subject matter, a method for identifying abusive content is provided, the method comprising: determining at least one feature associated with a playlist created by a user-generated channel; calculating a playlist score associated with the playlist based on a playlist classifier, wherein the playlist classifier comprises a function that maps the at least one feature to the playlist score; calculating a channel score associated with the user-generated channel based at least on the calculated playlist score; determining that one or more content items associated with the user-generated channel is to be demoted based on the calculated channel score, wherein the one or more content items comprises the playlist; and causing the one or more content items to be demoted.

The present teaching relates to a method, system and programming for operating a data storage. The data storage comprises of different portions including: a first portion having a plurality of metadata objects stored therein, each of the metadata objects being associated with a filter and corresponding to a range of keys, wherein at least one of the metadata objects is associated with a data structure, and a second portion having a plurality of files stored therein, each of the plurality of files being associated with one of the plurality of metadata objects; The data storage synchronizes a scan request with respect to one or more write requests based on a parameter associated with the scan request and each of the one or more write requests.