Abstract: Described herein are methods and systems for generating a mobile device-specific identifier usable across native mobile applications and mobile browsers with a unified opt-out mechanism. A mobile device generates a transient identifier for the mobile device and maps the transient identifier to a stable identifier of the mobile device using at least one call to a service endpoint coupled to the mobile device via a communications network. A browser application on the mobile device renders a webpage, the webpage comprising first instructions, executed by the browser application, to retrieve the stable identifier of the mobile device using at least one call to the service endpoint coupled to the mobile device via a communications network.

Abstract: A method is performed by a computing device. A request is sent comprising request data, the request data comprising one or more identifiers. Payload data is received, where the payload data includes advertising creative retrieval data and application retrieval data for downloading one or more application installation files for an application, where the advertising creative is associated with the application. The advertising creative is downloaded. If the application is not installed on the computing device, the one or more application installation files are downloaded. An advertising creative display opportunity is generated. The advertising creative is displayed in the advertising creative display opportunity. If a user interaction with the advertising creative is detected and the application is not installed on the computing device, the application is installed from the downloaded one or more application files.

Abstract: Described herein are methods and systems for generating a mobile device-specific identifier usable across native mobile applications and mobile browsers with a unified opt-out mechanism. A mobile device generates a transient identifier for the mobile device and maps the transient identifier to a stable identifier of the mobile device using at least one call to a service endpoint coupled to the mobile device via a communications network. A browser application on the mobile device renders a webpage, the webpage comprising first instructions, executed by the browser application, to retrieve the stable identifier of the mobile device using at least one call to the service endpoint coupled to the mobile device via a communications network.

Abstract: Described herein are methods and systems for generating a mobile device-specific identifier usable across native mobile applications and mobile browsers with a unified opt-out mechanism. A mobile device generates a transient identifier for the mobile device and maps the transient identifier to a stable identifier of the mobile device using at least one call to a service endpoint coupled to the mobile device via a communications network. A browser application on the mobile device renders a webpage, the webpage comprising first instructions, executed by the browser application, to retrieve the stable identifier of the mobile device using at least one call to the service endpoint coupled to the mobile device via a communications network.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Described are methods, systems, and apparatus for recommending catalog items for blacklisting. For each of a plurality of catalog items: image features are extracted; image features are associated with the catalog item; and user purchase events, user view events, a textual description, and categories are associated with the catalog item by the recommendation system. An identification of a first blacklisted catalog item is received. A catalog item is identified by the recommendation system based on i) a similarity between the image features associated with the first blacklisted catalog item and the image features associated with the catalog item, and ii) a correspondence between at least one of the user purchase events, the user view events, the textual description, or the categories associated with the blacklisted catalog item and the user purchase events, the user view events, the textual description, or the categories associated with the catalog item.

Abstract: In order to provide a more efficient persistent storage device, one or more long-term storage media are included along with a non-volatile memory. In one embodiment, one portion of the non-volatile memory is used as a write buffer and a read cache for writes and reads to the long-term storage media. Interfaces are provided for controlling the use of the non-volatile memory as a write buffer and a read cache. Additionally, a portion of the non-volatile memory is used to provide a direct mapping for specified sectors of the long-term storage media. Descriptive data regarding the persistent storage device is stored in another portion of the non-volatile memory.

Abstract: A computer accesses a multidimensional vector space that comprises embeddings of catalog item embeddings, each catalog item having one or more item attributes and corresponding item attribute values, and embeddings of associated item attribute values. An identification of one or more seed catalog items is received. Embeddings of each of one or more seed catalog items and embeddings of associated item attribute values are identified within the vector space. For each one of the seed catalog items, for each of the item attributes of the seed catalog item, an item representation in the vector space is located based upon a combination of the embedding of the seed catalog item and of an embedding of an item attribute value representing an item attribute value that is similar to the corresponding item attribute value of the seed catalog item. For each item representation, one or more embeddings of candidate catalog items that are close to the product representation are selected.

Abstract: Described are methods, systems, and apparatus for recommending catalogue items for blacklisting. For each of a plurality of catalog items: image features are extracted; image features are associated with the catalog item; and user purchase events, user view events, a textual description, and categories are associated with the catalog item by the recommendation system. An identification of a first blacklisted catalog item is received. A catalog item is identified by the recommendation system based on i) a similarity between the image features associated with the first blacklisted catalog item and the image features associated with the catalog item, and ii) a correspondence between at least one of the user purchase events, the user view events, the textual description, or the categories associated with the blacklisted catalog item and the user purchase events, the user view events, the textual description, or the categories associated with the catalog item.

Abstract: Techniques involving visual display of information related to matching user utterances against graph patterns are described. In one or more implementations, an utterance of a user is obtained that has been indicated as corresponding to a graph pattern through linguistic analysis. The utterance is displayed in a user interface as a representation of the graph pattern.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: A method is performed by a computing device. A request is sent comprising request data, the request data comprising one or more identifiers. Payload data is received, where the payload data includes advertising creative retrieval data and application retrieval data for downloading one or more application installation files for an application, where the advertising creative is associated with the application. The advertising creative is downloaded. If the application is not installed on the computing device, the one or more application installation files are downloaded. An advertising creative display opportunity is generated. The advertising creative is displayed in the advertising creative display opportunity. If a user interaction with the advertising creative is detected and the application is not installed on the computing device, the application is installed from the downloaded one or more application files.

Abstract: Embodiments of the invention provide a method and system for automatically instrumenting a set of web documents, such as web pages, as well as embedding structures that present advertising content via the web pages. The instrumentation automatically embeds tags that enable usage information associated with the web documents to be tracked and recorded. Many hundreds or thousands of web pages can be automatically modified without user intervention, enabling comprehensive reporting and tracking to be performed on each page. The web pages are analyzed and insertion points intelligently located. Changes can be verified to ensure that no undesirable effects resulted from embedding the content. The tags can receive parameters customized to the level of users and pages. The tags, insertion information, and other configuration information can be stored in a central repository to make subsequent tagging easier.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: The described implementations relate to automatic network selection in relation to wireless mobile devices. One method can be applied to a mobile device that has both Wi-Fi and cellular capabilities. The method can attempt to identify Wi-Fi network availability for the mobile device. This method also evaluates whether to configure the mobile device to accomplish data communication over an identified Wi-Fi network or a cellular network.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.