An online system generates a cluster group and uses membership in the cluster group as an eligibility criteria for presenting a content item. The online system receives a request from a third party system to present the content item. The online system also receives identification information about users who have visited webpages associated with the third party system and descriptive information associated with the webpages. Based on the descriptive information, the online system extracts tags for the webpages and classifies the webpages into one or more categories that include a category associated with the content item. The online system generates a seed group that includes users who visited webpages in the category associated with the content item. The online system further expands the seed group to a cluster group by applying a cluster model to one or more characteristics of each candidate user not included in the seed group.

Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

A skill dispatching method for a speech dialogue platform including: receiving, by a central control dispatching service, a semantic result of recognizing a user's voice sent by a data distribution service; dispatching, by the central control dispatching service, a plurality of skill services related to the semantic result in parallel, and obtaining skill parsing results from the plurality of skill services; sorting the skill parsing results based on priorities of the skill services, and exporting a result with the highest priority to a skill realization discrimination service; when failure in realization, selecting a result with the highest priority among the rest of skill parsing results and exporting the same to the skill realization discrimination service, and when success in realization, sending the result with the highest priority to the data distribution service for feedback to the user. The method improves skill dispatching efficiency, reduces delay, and improves user experience.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating search queries in response to obtaining audio samples on a client device. In one aspect, a method includes the actions of i) receiving audio data from a client device, ii) identifying specific content from captured media based on the received audio data, wherein the identified specific content is associated with the received audio data and the captured media includes at least one of audio media or audio-video media, iii) obtaining additional metadata associated with the identified content, iv) generating a search query based at least in part on the obtained additional metadata, and v) returning one or more search results to the client device, the one or more search results responsive to the search query and associated with the received audio data.

A computer can comprise a housing, a microprocessor disposed within the housing, a display, and a communication interface communicatively coupled to the microprocessor. The computer can be configured, responsive to locating decodable indicia within content viewable on the display, to decode the decodable indicia to produce at least one decoded message. The computer can be further configured to display the content with decoded message data being embedded into the content. The decoded message data can be provided by at least one decoded message, data derived from the decoded message.

A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, where the master node is the root node in a tree. The master node may distribute query plans and query metadata to other nodes in the cluster. These additional nodes may request additional metadata from each other or the master nodes as necessary.

A system, computer-readable storage medium storing at least one program, and a computer-implemented method for identifying availability of media items is presented. A search query is received from a client device of a user. Instances of media items that satisfy the search query and that are available on content sources accessible to the client device of the user are identified. Aggregate information for the media items is determined based on the instances of the media items. The aggregate information for the media items is transmitted to the client device.

A system, computer-readable storage medium storing at least one program, and a computer-implemented method for identifying availability of media items is presented. A search query is received from a client device of a user. Instances of media items that satisfy the search query and that are available on content sources accessible to the client device of the user are identified. Aggregate information for the media items is determined based on the instances of the media items. The aggregate information for the media items is transmitted to the client device.

Systems and methods of returning location and/or event results using information mined from non-textual information are provided. Non-textual information is captured using a hardware component of a user device. Text-based social media content input on the user device is then retrieved. A location of the user device is determined using a global positioning system module in the user device. The non-textual information is converted to a machine-analyzable format, and the converted non-textual information is compared to a database of converted non-textual information samples to analyze and classify the converted non-textual information. The classification is sent to a server for storage in a database in a manner that ties the classification to the geographical location of the user device.