Abstract: A method for presenting personalized content to a user includes receiving user data corresponding to a user having a user profile, the user data including at least one or more messages in a user mailbox and a user web browser history within a network, extracting one or more data tags from the received user data, associated at least one data tag with a message, finding information in the network that corresponds to the associated data tag, generating a notification for the user, the notification including the found information in the network, and outputting the generated notification to a user interface of a device of the user.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.

Abstract: One or more computing devices, systems, and/or methods for generating a recommendation for increasing loading time performance of a target webpage are provided. For example, a third party window, specifying a target webpage as a source, is inserted into a webpage. The webpage is provided to a browser that displays the webpage and loads the target webpage into the third party window. Resource timing data, associated with loading the target webpage into the third party window, is retrieved. Loading time performance of the target webpage is measured based upon the resource timing data. A recommendation for increasing loading time performance of the target webpage is generated. In an example, an action such as a resource allocation change for hosting the target webpage, compression of an image of the target webpage, a connection reduction of files connected to by the target webpage, etc. is performed based upon the recommendation.

Abstract: The present teaching relates to method, system, medium, and implementations for managing a virtual studio. A plurality of single data streams from a plurality content contributors are received. When a request is received, via public network connections, for creating a composite data stream associated with a virtual room in the virtual studio, signaling information is generated for constructing the composite data stream by stitching together some of the plurality of single data streams selected to be incorporated in the composite data stream in accordance with a layout. When an access request is received from an end user to access the composite data stream, the composite data stream is delivered to the end user in response to the access request.

Abstract: A method of providing targeted content across user channels includes receiving a plurality of device activity events on a user device, storing the received plurality of device activity events, creating an audience population based on the stored device activity events, determining links for additional secondary user devices based on the user device, creating a cross-channel advertising campaign targeting the determined audience population, and running the cross-channel advertising campaign based on the device activity and the determined additional secondary user devices.

Abstract: Disclosed are methods and devices for converting mobile notifications to content using a local content exchange. In one embodiment, the method comprises receiving a request for content from a mobile application; querying a data store of available content, the data store of available content populated with one or more notifications received by other mobile applications installed on the mobile device; identifying a responsive notification for the request for content based on the querying, the content item identified based on bid parameters associated with the one or more notifications; and transmitting the responsive notification to the mobile application.

Abstract: Users may consume and/or share information through various types of content items. For example, user may post a family photo through a social network, create a running blog through a microblogging service, etc. Because users may be overwhelmed by the amount of available content items, it may be advantageous to recommend content items, such as blogs to follow, to users. Accordingly, inductive matrix completion is used to evaluate user interactions with content items (e.g., a user following a blog), content item features (e.g., text and/or images of a blog is evaluated to identify a topic of the blog), and/or user features (e.g., a user liking or reblogging a blog, user demographics, user interests, etc.) to determine whether to recommend a content item to a user. Additionally, graph proximity is used to recommend content items based upon weights of edges connecting user nodes to content item nodes within a directed graph.

Abstract: Techniques and technologies described herein can generate and provide identifiers, such as unique identifiers, for individual records of aggregate data. Such identifiers allow systems to cache and reuse individual records of aggregate data. Also, such identifiers can facilitate simpler analytic views of aggregate data.

Abstract: The present teaching relates to managing computing resources. In one example, a search query is received from a user. A plurality of performance scores associated with a plurality of providers is obtained from a provider score database. One of the plurality of providers is selected based on the plurality of performance scores. The search query is transmitted to be performed by the selected one of the plurality of providers. One or more search results are received in response to the search query from the selected one of the plurality of providers. The one or more search results are presented to the user to be displayed on a user device.

Abstract: Systems and methods are disclosed for accessing first party Internet cookies. One method includes receiving, over an electronic network, a request for an electronic advertisement or an Internet cookie, wherein the request is received in response to a user requesting content from a subdomain of an advertising entity domain; and sending, to a device associated with the user, an Internet cookie having the subdomain of the advertising entity domain. The method further includes receiving, from the device associated with the user, a request for content from the subdomain of the advertising entity domain, wherein the request is received in response to the user requesting content from a domain of an online publisher; and accessing data stored in the Internet cookie sent to the device associated with the user.

Abstract: A system for managing tags on a web page is disclosed. Tags are managed by implementing primary and secondary tags. The secondary tags are aggregated to provide network and tag management efficiency.

Abstract: One or more computing devices, systems, and/or methods for transmitting response content items to devices are presented. For example, a graphical user interface may be controlled to display an interface. The targeting interface may comprise a selectable input. The selectable input may correspond to a content item. A request for a targeted response to the content item may be received via the interface. The request may comprise a response content item and a selection of the selectable input. A plurality of assignments of the content item to a plurality of users may be determined. The response content item may be transmitted to a plurality of devices. Each device of the plurality of devices may be associated with a user of the plurality of users.

Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.

Abstract: Systems and methods are disclosed for discovering precursors associated with a current user interaction event. One method comprises receiving a selection of a new item by a user and determining a plurality of similarities between the new item selected by the user and a plurality of historical items, the plurality of historical items being associated with prior user activity. Then a plurality of importance weights associated with the plurality of historical items are determined. Based on the plurality of similarities and the plurality of importance weights, at least one enhanced importance matrix is generated. Further, prior interactions of the user with the plurality of historical items are determined. Based on the enhanced importance matrix and the prior interactions of the user with the plurality of historical items, precursors for the new item selected by the user are identified and provided to a display.

Abstract: Methods and systems for recommending online media content to other users includes receiving a selection of media content rendered on content page. The media content is identified by a user for sharing. A list of topics associated with the user is generated for presenting on a user interface. The topics are descriptive of the media content selected for sharing. Selection of one or more topics for the selected media content is received from the user. The received selections define the user's relevancy perspective for the selected media content. A recommendation for the selected media content is provided in content streams of users that follow the selected topics for users interactions with the selected media content.

Abstract: An electronic system may be configured to determine if a first-type device ID and a second-type device ID both identify a same device. To do so, the electronic system may preliminarily group a particular first-type ID with a particular second-type ID based on matching time slots and network addresses. Individual scores for each of the preliminary groups may be determined based on cardinalities associated with the time slots. Combined scores may then be determined for groups having the same first-type device ID and second-type device ID. A final analysis may be performed to confirm, with a sufficient level of confidence, whether first-type device IDs and second-type device IDs included in the preliminary groups identify the same device.

Abstract: Systems and methods are disclosed for associating a plurality of Internet-enabled devices with a common user profile for targeting Internet content or advertising. One method includes: receiving, from a plurality of Internet-enabled devices, a plurality of requests for electronic content or advertising; extracting, from each of the plurality of requests, a source IP address and a unique identifier associated with the respective Internet-enabled device; for each source IP address for which requests were received over a predetermined time period from a number of Internet-enabled devices below a threshold number of devices, identifying each possible pair of devices from which requests were received; and for each possible pair of devices, calculating a probability that the pair of devices are owned or operated by a common user.

Abstract: The present teaching relates to a method and system for validating labels of training data. A first group of data records associated with the training data are received, wherein each of the first group of data records includes a vector having at least one feature and a first label. For each of the first group of data records, a second label is determined based on the at least one feature in accordance with a first model. Thereafter, a loss based on the first label associated with the data record and the second label is obtained, and the data record having an incorrect first label is classified when the loss meets a pre-determined criterion. Upon classifying the data records, a sub-group of the first group of data records is generated, wherein each of the data records included in the sub-group has the incorrect first label.

Abstract: A method, system and non-transitory computer-readable medium for operating a storage engine are disclosed. With respect to a data file, a compaction value is obtained and a counter is retrieved. The compaction value indicates the minimum number of valid records in the data file for a clean-up process to be initiated. In response to the counter satisfying a first criterion, a determination is made, for each record in the data file, as to whether the record is written to another data file based on the record satisfying a second criterion. A write amplification factor of the storage engine is determined based on the compaction value. The counter indicates the number of records in the data file that are invalid. The write amplification factor and a space amplification factor are configurable; the storage engine may be tuned based on workloads, desired write throughput, desired storage utilization, and bandwidth of a storage device.