The present teaching relates to displaying ads. An explore/exploit layer (EEL) is provided at frontend ad serving engine for storing combination distributions with respect to multiple ads. Each ad has multiple attributes. Each attribute can be instantiated using one of multiple assets. The frontend ad serving engine requests a recommended ad for bidding an ad display opportunity in a slot of a webpage viewed by a user on a user device. The recommended ad is one of the multiple ads. When the auction is successful, a combination of assets for the ad is drawn from the combination distributions in EEL and each of the assets instantiates a corresponding attribute of the ad. The combination is transmitted to the user device to render the ad.
The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.
The present teaching relates to generating combination distributions for ads. Features are computed based on training data associated with ads, each of which has a plurality of attributes. The training data include asset combinations with past performance thereof for each of the ads. Each combination includes multiple assets representing respective attributes of an ad. The features are used in machine learning to obtain an auxiliary model, which is used to generate combination distributions for each ad based on predicted performance for each combination associated with the ad. Such generated combination distributions are sent to an explore/exploit layer (EEL) for a frontend ad serving engine to draw a combination therefrom for an auction winning ad for rendering on a webpage viewed by a user on a user device.
In an example, a system, a method and/or an apparatus are provided. A first power distribution component and a second power distribution component are connected to a busway system including a busway. Whether first electrical power of the first power distribution component and second electrical power of the second power distribution component meet one or more conditions is determined. In response to a condition of the one or more conditions not being met, supply of electrical power from the second power distribution component to the busway system is inhibited.
In an example, a first item of a plurality of items may be identified. A set of items related to the first item may be determined based upon first item information associated with the first item. A second set of items, of the plurality of items, may be determined, wherein the second set of items is targeted to a user. The second set of items includes the first item. Based upon the set of items and the second set of items, a set of candidate content items may be determined for inclusion in an auction associated with selection of content for presentation via a client device associated with the user. The set of candidate content items includes one or more content items associated with the set of items related to the first item. A content item may be selected from content items comprising the first set of candidate content items.
One or more computing devices, systems, and/or methods for defect detection are provided. An image, depicting an object for evaluation to determine whether the object has a defect, is inputted into a segmentation model to identify an object region of interest of the object. An object region area of the object region of interest is calculated. A convex hull area of a convex hull encompassing the object region of interest is calculated. A ratio of the object region area to the convex hull area is determined. The ratio is compared to a threshold to determine whether the object has the defect or does not have the defect.
One or more computing devices, systems, and/or methods are provided. First event information associated with a plurality of events may be determined, wherein the plurality of events is associated with a first entity. A set of event metrics associated with the first entity may be determined based upon the first event information. A first combined metric may be determined based upon at least two metrics of the set of event metrics. Whether the first entity is fraudulent may be determined based upon the first combined metric and a threshold metric associated with anomalous behavior.
Systems and methods are provided for improving web-based document retrieval and object manipulation. In an implementation, objects within web documents (e.g., a web page) are manipulated when the objects are visible through a viewport of a user's web browser. According to a method, an object selected from a web document is manipulated when that selected object is displayed within the viewport of the user's browser. The manipulation may include downloading content associated with the stored object and additionally, or alternatively, executing a script associated with the stored object. Additionally, or independently, methods may be provided for blocking the download of an object in a retrieved web document until that object is displayed or visible in the user's browser viewport.
One or more computing devices, systems, and/or methods are provided. In an example, a dataset associated with a plurality of dimensions and a plurality of metrics is identified. A subset of dimensions of the plurality of dimensions and a subset of metrics of the plurality of metrics are selected based upon historical dataset queries. A plurality of sets of results is generated, using the dataset, based upon the subset of dimensions and the subset of metrics. A plurality of significance scores is determined based upon the plurality of sets of results. One or more first sets of results of the plurality of sets of results are selected based upon the plurality of significance scores.
One or more computing devices, systems, and/or methods are provided. In an example, a sequence of actions performed using a first interface on a first client device may be identified. A first negative signal probability may be determined based upon the sequence of actions. The first negative signal probability may correspond to a probability of receiving a negative signal associated with a first content item from the first client device responsive to presenting the first content item via the first interface on the first client device. The first interface on the first client device may be controlled based upon the first negative signal probability.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies
The disclosed systems and methods provide a framework for a proactive prediction of the toxic propensity of an article. Prior to the publication and/or reception of comments to online content, the disclosed framework determines the toxic propensity of the content's context and/or specific words, sentences, sentiments, tone or other messages receivable from consumption of the content. Thus, disclosed framework performs proactive forecasting of the content's toxicity propensity”, which quantifies how likely the content is prone to incur or attract toxic comments. The framework can function and/or be configured to operate in a manner that can perform specifically adherent moderation actions that correspond to the content and control how the content can be interacted with, based on the toxic propensity determination, prior to the content's publication in an effort to thwart, prevent or stop toxic environments surrounding or stemming from the content from coming into existence.
The disclosed embodiments describe techniques for allocating memory to functions processing big data. In one embodiment, a method is disclosed comprising allocating a first memory space to a function, the memory space comprising an initial amount of memory for the function; declaring the first memory space as the current memory space; processing data using the function, the processing writing data to the current memory space; determining that the function requires additional memory space; allocating a new memory space based on the current memory space and a growth factor; copying all data in the current memory space to the new memory space; declaring the current memory space as the old memory space; declaring the new memory space as the current memory space; and not deallocating the old memory space.
One or more computing devices, systems, and/or methods for generating summary lists based upon articles are presented. In an example, a summarizing set of sentences of an article may be identified. The summarizing set of sentences may be analyzed to identify one or more first sentences of the summarizing set of sentences that meet a set of conditions and/or identify one or more second sentences of the summarizing set of sentences that do not meet the set of conditions. A summary list summarizing the article may be generated based upon the one or more first sentences.
One or more computing devices, systems, and/or methods are provided. In an example, financial account information associated with a plurality of payment options may be determined. First financial account information of the financial account information is associated with a first payment option of the plurality of payment options. The first financial account information may be indicative of a first rewards profile associated with transactions performed using the first payment option. A request associated with a transaction may be received. A plurality of financial return scores associated with the plurality of payment options may be determined based upon the financial account information and transaction information associated with the transaction. A first financial return score of the plurality of financial return scores may be associated with the first payment option. The first payment option of the plurality of payment options may be selected for the transaction based upon the plurality of financial return scores.
The instant system and methods solves the cold start problem through various systems and methods directed to aggregating user interaction data associated with a user over a period of time, generating an embedding model based on the aggregated user interaction data, generating a content embedding vector based on the embedding model, generating an embedding profile vector based on the embedding model, storing the embedding profile vector in a storage device, receiving each of the content embedding vector and embedding vector profile for training a ranking model, and generating a predicted list of one or more content items of interest for recommending to the user.
The disclosed embodiments describe techniques for isolating and managing models via versioning. In one embodiment, a method is disclosed comprising reading a configuration document associated with a first model; incrementing an internal write version of the configuration document; storing the internal write version in the configuration document; generating documents belonging to a second model such that respective document identifiers of the documents include a next external version a first field and the incremented internal write version in a second field; and uploading the documents to a serving system, causing the serving system to replace the first model with the second model.
The disclosed systems and methods provide a novel framework that provides mechanisms for a Deep & Cross Network (DCN) framework that performs distilled deep prediction for personalized stream ranking on portal websites. The disclosed framework is scalable to satisfy the much more stringent latency and computational requirements required by current network operating environments. The disclosed framework is able to dynamically evaluate and leverage live traffic on network sites in order to provide, update and maintain current recommendations for users as they traverse to a portal and when they navigate within the portal. The disclosed framework implements a DCN model(s) that is capable of being compressed into a model size for a unified optimization within a live traffic environment by combining knowledge distillation and model compression techniques. The disclosed framework is built as a light-weight deep learning model that can be served in production and perform on par with large models.
The disclosed systems and methods provide a novel framework that leverages collected logged-in daily active users (LIDAU) data to drive network traffic to network resources, as well as engage these users to continue or remain engaged with the resources through personalization and customization according to their behaviors and patterns. LIDAU data, which is based on a raw data feed of information from network resources and an identified dataset determined from the raw data feed, is used as a basis to increase LIDAU for a specific user or a set of other users. The determined understanding of LIDAU, and its impact on users as well as the network resources the users are interacting with, enables the framework to personalize the resources that are anticipated as being visited by the users in advance of the users visiting them.
One or more computing devices, systems, and/or methods for debiasing training data based upon information seeking behaviors are provided. Users associated with a set of training data are segmented into information seeking behavior groups corresponding to varying degrees of information seeking behaviors of the users. Biases for the information seeking behavior groups may be estimated based upon information seeking behaviors of users within the information seeking behavior groups. The training set of data is debiased using the biases to generate a debiased training set of data. A model may be trained to perform a task based upon the debiased training set of data.
One or more computing devices, systems, and/or methods are provided. Machine learning model training may be performed using first training data to generate a first machine learning model. Inference-training data may be generated, wherein the inference-training data may include a plurality of sets of training data of the first training data, a plurality of sets of inference data, and/or target information indicative of the plurality of sets of training data being associated with a first classification and the plurality of sets of inference data being associated with a second classification. Machine learning model training may be performed using the inference-training data to generate a second machine learning model. Predictions associated with one or more sets of data may be determined using the second machine learning model. An evaluation of the first machine learning model and the one or more sets of data may be generated based upon the predictions.
One or more computing devices, systems, and/or methods for implementing an automated model update pipeline are provided. User behavior data associated with content provided to users may be collected. An automatic model training is invoked to train a new model to output a set of model parameters based upon a configuration specifying a target audience, features extracted from user behavior data, and training model parameters. In response to determining that the new model will outperform a deployed model on a content serving platform, an automatic model updater is invoked to update the content serving platform with the new model and a ranking profile of the new model for serving content requests
Disclosed are systems and methods for improving interactions with and between computers in content providing and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework that automatically labels and classifies incoming emails. The disclosed framework embodies a novel computerized taxonomy configured as a multi-tier, multi-label classification system. The first tier involves an offline grid classifier that has higher accuracy, and the second tier is an online classifier that classifies emails in real-time. Thus, the framework provides a novel approach to classifying messages based on a multi-tiered analysis, which is utilized for generating user profiles, delivering the messages, and the like.
The present teaching relates to method, system, medium, and implementations for machine learning. Upon receiving input data associated with a time series, hidden representations associated with the time series in a feature space are obtained and used to generate a query vector in a query space. Such generated query vector is then used to query relevant historic information related to the time series. The query vector and the relevant historic information are aggregated to generate at least one queried vector, which is aggregated with the hidden representations to generate enriched hidden representations that enhance the expressiveness of the hidden representations.
The present teaching relates to method, system, medium, and implementations for machine learning for time series via hierarchical learning. First, global model parameters of a base model are learned via deep learning for forecasting time series measurements of a plurality of time series. Based on the learned base model, target model parameters of a target model are obtained by customizing the base model, wherein the target model corresponds to a specific target time series from the plurality of time series for forecasting time series measurements of the specific target time series.
Systems and methods are disclosed for enabling users to request information or services relating to a physical facility. One method includes receiving, from a device associated with a user, one or more of: a request to receive information about an asset or component of the facility, and a request to receive maintenance of an asset or component of the facility, wherein the request includes an identifier associated with the user and an identifier associated with the asset or component of the facility; accessing, based on the request, a database storing one or more user identifiers and one or more identifiers associated with a plurality of assets or components of the facility; and transmitting, to the device associated with the user, either a presentation of information about the asset or component of the facility, or a representation of a ticket for initiating maintenance of the asset or component of the facility.
G06Q 90/00 - Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
26.
COMPUTERIZED SYSTEM AND METHOD FOR DISPLAY OF MODIFIED MACHINE-GENERATED MESSAGES
Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a universally applied framework for analyzing all forms and types of messages being communicated over network, and providing functionality to an inbox for alerting a user to specific forms or types of content included within received and displayed messages. Such functionality can include determining and displaying specific message content in a modified manner when its associated message is displayed from a message inbox. The messages can be modified upon display within in the inbox to indicate and identify that the message includes content of a specific type or form.
Techniques for clustering non-stationary data are disclosed. In embodiments, a method is disclosed comprising initializing a plurality of functional centroids; partitioning a non-stationary data set, using the functional centroids, into partitions, the number of partitions being equal to the number of functional centroids; generating a set of fitted functional centroids for each of the partitions; replacing at least one of the functional centroids with a corresponding fitted functional centroid if a computed energy of the corresponding fitted functional centroid is less than an energy of the at least one functional centroid; computing a summation of the energies associated with each of the functional centroids; and outputting the functional centroids upon determining that a termination condition is met.
Disclosed are systems and methods for generating recommendations to users based on historical travel information and electronic communication data. The disclosed systems and methods provide a novel framework for automating the transmission of electronic travel-related recommendations to users by consistently monitoring electronic messages received at an electronic communication mailbox corresponding to a user. The disclosed framework operates by leveraging historical user data, data parsed from electronic communication mailbox corresponding to a user, or various vendor information, and using the aforementioned data as inputs for travel-related recommendation models, in order to generate and transmit the optimal travel-related recommendations to a user.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
29.
Systems and methods for deep learning based approach for content extraction
Disclosed are systems and methods for extracting content based on image analysis. A method may include receiving content including at least an image depicting a coupon; converting the received content into a larger image including the image depicting the coupon; determining, utilizing one or more neural networks, the image depicting the coupon within the larger image, wherein determining the image depicting the coupon comprises: segmenting a foreground bounding box including the image depicting the coupon from background image portions of the image; cropping the larger image based on the bounding box, wherein the cropped image consists of the image depicting the coupon; determining text included in the cropped image; and extracting information included in the coupon based on the determined text.
Methods, systems, and computer-readable media are disclosed for dynamic partitioning in distributed computing environments. One method includes: receiving a first data set and a second data set; mapping the first data set into a first set of key-value pairs; mapping the second data set into a second set of key-value pairs; estimating, using a sketch, a frequency count for each key based on the first set of key-value pairs and the second set of key-value pairs; determining whether the estimated frequency count for each key is greater than or equal to a predetermined threshold; and partitioning the key when the estimated frequency count for the key is greater than or equal to the predetermined threshold.
One or more computing devices, systems, and/or methods for selecting content items for presentation via client devices are provided. A content event associated with a content item performed by a client device may be detected. The content item may be associated with an entity. A conversion event, associated with the entity, performed by the client device may be detected. A duration of time between the content event and the conversion event may be determined. An attribution score may be determined based upon the duration of time. A plurality of attribution scores, comprising the attribution score, may be stored in an attribution data structure associated with the content item. Responsive to receiving a request for content associated with a second client device, the content item may be selected from a plurality of content items for presentation via the second client device based upon the attribution data structure.
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.
H04N 5/222 - Studio circuitry; Studio devices; Studio equipment
H04N 21/262 - Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission or generating play-lists
H04N 21/218 - Source of audio or video content, e.g. local disk arrays
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.
One or more computing devices, systems, and/or methods for generating presentations based upon articles are presented. For example, an article may be selected from one or more article databases. Content items may be extracted from the article, wherein the content items comprise one or more videos, one or more images and/or one or more social media posts. Text of the article may be analyzed to generate a plurality of text segments. A presentation, comprising a plurality of slides, may be generated based upon the content items and the plurality of text segments. A graphical user interface of a client device may be controlled to display a presentation editing interface comprising a representation of the presentation. One or more inputs, corresponding to one or more edits to the presentation, may be received via the presentation editing interface. An edited presentation may be generated based upon the one or more inputs.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
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.
The present teaching relates to request management and data recovery in a data system. In one example, a failure in connection with first data is detected at a first node in a data system. Information associated with a most recent transaction related to the first node is obtained from the persistent storage. Each of other nodes in the data system is requested to transmit one or more transaction requests previously sent to the first node after the most recent transaction. The one or more transaction requests are received from at least one of the other nodes. A sequence of one or more transactions associated with the one or more transaction requests is determined. The one or more transactions are executed according to the sequence in order to recover the first data at the first node.
G06F 16/00 - Information retrieval; Database structures therefor; File system structures therefor
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
In general, the present disclosure includes a system, method and architecture for use in analyzing content of a social network data to identify a financial market trend, e.g., a trend associated with a financial market instrument.
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.
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.
The present teaching generally relates to detecting providing pre-validated data buckets for online experiments. In a non-limiting embodiment, user activity data representing user activity for a first plurality of user identifiers may be obtained. A first set of values and a second values, representing first and second user engagement parameters, respectively, may be generated for each user identifier based on the user activity data. A first ranking and a second ranking may be determined for the first and second sets, respectively. A first exclusion range including a first number of values to be removed from the first and second sets may be determined. A homogenous value set may be generated by removing the first number of values from the first and second sets, where each value from the homogenous value set corresponds to a user identifier available to be placed in a data bucket for an online experiment.
G06F 16/14 - File systems; File servers - Details of searching files based on file metadata
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06F 12/1018 - Address translation using page tables, e.g. page table structures involving hashing techniques, e.g. inverted page tables
G06F 16/435 - Filtering based on additional data, e.g. user or group profiles
G06F 16/2457 - Query processing with adaptation to user needs
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
Systems and methods are disclosed for online distribution of content by receiving, from a user's mobile device, a request for a web page hosted by a publisher's CMS; applying a rules engine to analyze a received URL according to a set of rules identifying one or more website types and/or referrers; if the received URL satisfies the rules engine, redirecting the received request to a syndication server system hosted within a global CDN; adding a URL of the web page to a missing content queue and redirecting the request to the publisher's CMS if the CDN syndication server does not contain a suitable mobile-formatted version of the web page; and delivering a package of binary compressed content of the web page to a stub page cached at the user's mobile device by the CDN syndication server, using recirculation and monetization components chosen by the publisher.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04L 29/06 - Communication control; Communication processing characterised by a protocol
H04M 1/72445 - User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality for supporting Internet browser applications
42.
Computerized system and method for automatically identifying and providing digital content based on physical geographic location data
Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically selecting the most definitive, precise and high-quality content files corresponding to POIs. The disclosed systems and methods utilize the performance of visual comparisons with a set of definitive content files of a given POI, and by incorporating visual aesthetic features as a factor of such comparisons, a search result is identified that down-weights imprecise and poor quality content files of a given POI, and ensures that only high quality, accurate content files are selected or identified.
G06F 16/58 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
G06F 16/587 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
G06F 16/535 - Filtering based on additional data, e.g. user or group profiles
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 16/9537 - Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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.
Users consume a wide variety of content from various sources, such as videos accessible through websites. As provided herein, content recommendations that are contextually and/or semantically relevant to current content consumed by a user may be identified and provided to the user. For example, metadata for a video being watched by the user may be identified (e.g., terms extracted from a description, user reviews, a category, and/or other information). The metadata may be used to identify content recommendations based upon the metadata corresponding to terms grouped into a set of refined topic groupings of a graph comprising terms and relationships between terms extracted from a content corpus. The metadata may be matched to relevant terms within the set of refined topic groupings, and content recommendations comprising content corresponding to the relevant terms may be suggested to the user.
Entity profile information is presented to a user of a computing device. In one embodiment, a method includes: receiving a communication associated with a first entity; in response to receiving the communication, creating or updating, by at least one processor, a first entity profile, wherein the first entity profile is for display to the user on the computing device; and storing a plurality of entity profiles for entities, the entity profiles including the first entity profile.
The present teaching relates to generating user profiles with semantic knowledge. A first information associated with a user is obtained. One or more entities are identified from the first information. The one or more entities are augmented based on second information to generate a set of augmented entities. The set of augmented entities are clustered into a set of hierarchical clusters. A set of user profiles is generated based on the set of hierarchical clusters so that the user profile is to be used to personalize content recommendation.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06Q 30/06 - Buying, selling or leasing transactions
G06F 16/9035 - Filtering based on additional data, e.g. user or group profiles
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
48.
Encoding and distributing snippets of events based on near real time cues
Moments of interest of an event may be automatically detected, encoded as snippets, and distributed to users subscribed to a channel of this event. In various embodiments, a moment of interest of an event is to be automatically detected, e.g., based on a near real-time cue on a social network or features in the event transmission. Further, a snippet can be encoded from the transmission of the event based on the detected moment of interest. The snippet may be put on a market place to be bid by various content distributors. Eventually, the snippet may be distributed to an interested user, e.g., via a channel subscribed by the user. Other embodiments may be described and/or claimed.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06Q 50/00 - Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
49.
Methods, systems and techniques for blending online content from multiple disparate content sources including a personal content source or a semi-personal content source
The present teaching, which includes methods, systems and computer-readable media, relates to providing content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include receiving a request for content from a person; obtaining first content from a first source private to the person based on the request; obtaining second content from at least one second source based on the request; blending the first content from the first source and the second content from the at least one second source to generate a blended content; and providing the blended content to the person in response to the request.
A method, implemented on at least one computing device each of which has at least one processor, storage, and a communication platform connected to a network for providing synthetic answers to a personal question is disclosed. A personal question is received from a person. One or more entities are extracted from the personal question. One or more relations are extracted from the personal question. A model is selected based on the personal question. One or more synthetic answers to the personal question are obtained based on the one or more entities, the one or more relations, and the selected model.
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.
patents