Abstract: In one embodiment, a user of a client device may selectively share personal data with web companies. The personal data may be shared directly via transmission by the client device to the web companies. Alternatively, the personal data may be shared with the web companies via a personal data sharing system that collects and maintains personal data associated with users. The personal data sharing system may reward the user for sharing their personal data with the web companies.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: The present disclosure generally relates to processing and organizing electronic content. In accordance with one implementation, a computer-implemented method is provided that comprises receiving source data from at least one content server, the source data being associated with electronic content. The method also includes generating local data based on at least one of an analysis of the received source data or an extraction from the received source data. Additionally, the method includes classifying the electronic content as being associated with one or more content stacks. Further, the method includes generating representations of the electronic content based on the local data and generating instructions to display at least one content stack on a user interface, each displayed contact stack being operable to display one or more of the representations of the electronic content associated with the content stack based on the classification.

Abstract: Disclosed are systems and methods for generating search result data based on machine-encoded text generated by computer vision optical character recognition machine learning techniques performed on digital media. The disclosed systems and methods provide a novel framework for performing machine learning visual search or machine learning text extraction techniques on digital media in order to extract and analyze the data therein and further conduct search queries based on the extracted and analyzed data. The disclosed framework may leverage the aforementioned computer vision machine learning techniques in order to provide a user with relevant search results regarding objects and text detect in digital media captured on a user device.

Abstract: One or more computing devices, systems, and/or methods for cross-domain action prediction are provided. Action sequence embeddings are generated based upon a textual embedding and a graph embedding utilizing past user action sequences corresponding to sequences of past actions performed by users across a plurality of domains. An autoencoder is trained to utilize the action sequence embeddings to project the action sequence embeddings to obtain intent space vectors. A service switch classifier is trained using the intent space vectors. In response to the service switch classifier predicting that a current user will switch from a current domain to a next domain, the current user is provided with a recommendation of an action corresponding to the next domain.

Abstract: Disclosed are embodiments for providing batch performance using a stream processor. In one embodiment, a method is disclosed comprising receiving, at a stream processor, an event, the stream processor including a plurality of processing stages; generating, by the stream processor, an augmented event based on the event, the augmented event including at least one additional field not appearing in the event, the additional field generated by an operation selected from the group consisting of a join or dimensional annotation operation; and emitting, by the stream processor, the augmented event to downstream consumer.

Abstract: One or more computing devices, systems, and/or methods for content recommendation based upon continuity and grouping information of attributes are provided herein. User interaction data specifying whether users interacted with content items, user attributes of the users, and content attributes of the content items is obtained. A data structure is populated with the user interaction data. The data structure is modified by inserting a set of sub-fields into the data structure for a user attribute. A sub-field is populated with a value representing an option of the user attribute. The set of sub-fields are an encoding of continuity information and grouping information representing options for the user attribute. The data structure is processed using machine learning functionality to generate a model. The model is utilized to generate a prediction as to whether a user will interact with a content item.

Abstract: The present teaching relates to dynamically generating a card. In one example, a request is received for generating a card to be provided to a user. Dynamic information related to the request is obtained. One or more modules are selected to be put into the card based on the dynamic information. The card is generated based on the selected one or more modules.

Abstract: Method, system, and programs for determining a keyword from user agent strings are disclosed. In one example, a plurality of user agent strings is received. The plurality of user agent strings is grouped into one or more clusters. The one or more clusters comprise a first cluster that includes two or more user agent strings. The two or more user agent strings in the first cluster are compared. Based on the comparing, a keyword is determined from the first cluster. The keyword represents a type of user agent information.

Abstract: The present teaching relates to methods, systems, and programming for information retrieval. A search result associated with a search query is obtained and provided to a user. Upon receiving a filtering request from the user, the search result is filtered based on the filtering request to generate an updated search result. The updated search result is provided to the user in response to the filtering request.

Abstract: One or more computing devices, systems, and/or methods for monitoring levels of activity of client devices using a cluster of servers having a decentralized network architecture are provided, where over-counting, which may be caused by an uneven distribution of requests transmitted by the client devices to the cluster of servers, may be mitigated. For example, a request may be received by a first server, of the cluster of servers, from a client device. A first counter value associated with a level of activity of the client device may be incremented by a first number. One or more data packets may be transmitted to one or more servers of the cluster of servers. Each data packet of the one or more data packets may comprise an instruction to increment a counter value associated with the client device by a second number, which may be different than the first number.

Abstract: One or more computing devices, systems, and/or methods for presenting content of an application are provided. For example, a first content interface, associated with an application, may be displayed using a first device. First activity performed using the first content interface may be detected. The first activity may be analyzed to generate a first activity profile associated with the first content interface. A first request to access the application may be received from the first device. The first activity profile may be selected from a plurality of activity profiles associated with the user account, based upon a determination that the first request is associated with the first content interface. Content items of the content items database may be prioritized, based upon the first activity profile, to generate a list of content items associated with the first content interface. The list of content items may be displayed by the first device.

Abstract: The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.

Abstract: One or more computing devices, systems, and/or methods for automated materialized view table generation and maintenance are provided. A log, comprising queries and latencies of processing the queries, is evaluated to identify a list of combinations of fields that occur greater than a threshold frequency and/or occur in queries having latencies greater than a threshold latency. A materialized view generation script is executed against a main database to generate a materialized view table associated with a combination of one or more fields from the list. A middleware component is configured to selectively direct a query to the main database or to the materialized view table based upon whether the materialized view table comprises preliminary query results for fields specified by the query.

Abstract: One or more computing devices, systems, and/or methods for providing recommendations to travelers are provided. For example, locational data associated with a device of a user is evaluated to identify a set of location points at which the device was located over time. Location point pairings are generated from the set of location points, where a location point pairing may comprise a departure location point and an arrival location point. The location point pairings are filtered based upon various criteria to remove location point pairings that are not indicative of air flight travel (e.g., a location point pairing not satisfying an air flight speed threshold or an air flight travel distance threshold). The user may be determined as an air flight traveler that has recently used air flight travel to reach a destination location. A recommendation of content for the destination location is provided to the user.

Abstract: The present teaching relates to managing computing resources. In one example, information about resource utilization on a computing node is received from the computing node. Available resource on the computing node is determined based on the information. A model generated in accordance with reinforcement learning based on simulated training data is obtained. An adjusted available resource is generated based on the available resource and the model with respect to the computing node. The adjusted available resource is sent to a scheduler for scheduling one or more jobs to be executed on the computing node based on the adjusted available resource.

Abstract: Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.

Abstract: Disclosed are embodiments for generating metadata files for composite datasets. In one embodiment, a method is disclosed comprising generating a tree representing a plurality of datasets; parsing the tree into an algebraic representation of the tree; identifying a plurality of terms in the algebraic representation, each term in the terms comprising at least two factors, each of the two factors associated with a dataset in the plurality of datasets; generating a metadata object of the plurality of terms; serializing the metadata object to generate serialized terms; and storing the serialized terms in a metadata file associated with the plurality of datasets.

Abstract: Systems and methods for securing digital content using hidden folders are disclosed. In one embodiment, a method comprises displaying an application, the application including a user interface for displaying digital content items and receiving input events; monitoring keystrokes entered by a user of the client device while the application is displayed; generating a candidate passcode based on the keystrokes; determining that the candidate passcode is a valid passcode has been entered when the candidate passcode is equal to a known passcode; transmitting a request for one or more hidden items from a server device, the request including the valid passcode; receiving the one or more hidden items; and updating the user interface to display the one or more hidden items.

Abstract: Systems and methods are disclosed for utilizing sender-recipient pair data to establish sender-level trust in future communication. One method comprises receiving raw communication data over a network and testing the received raw communication data against trained machine learning data to predict whether the raw communication data is associated with expected communication data. The raw communication data is sorted for expected communication data, which is further analyzed for sender-recipient pair data and assigned an expected communication pair data score. Senders associated with an expected communication pair data score that meets or exceeds a threshold are labeled and stored in a database as trusted. As a result of the sender-recipient pair analysis, recipients at-risk for being scammed can be identified, senders misidentified as spammers can be properly classified, and machine learning techniques utilized for analyzing raw communication data can be fine-tuned.