Abstract: A system and method for generation of automated forecasts for a subject based on one or more input parameters. The subject located at an end node of a hierarchy. The method includes: receiving historical data associated with the subject; determining the sufficiency of the historical data based on a feasibility of building a machine learning model to generate a forecast with a predetermined level of accuracy using the historical data; building the machine learning model using the historical data when there is sufficiency of the historical data; building the machine learning model using historical data associated with an ancestor node on the hierarchy when there is not sufficiency of the historical data; generating a forecast for the subject using the machine learning model based on the one or more input parameters; and outputting the forecast.

Abstract: Systems and methods for constraint-based optimization, comprising: an AI demand forecasting engine, an optimization engine, a user-defined objective, and a user-defined set of constraints. Using historical sales data, the AI demand forecasting engine generates a plurality of entities, each entity defined by a placement of an item in a promotion platform; and forecasts the objective associated with each entity. The optimization engine generates a plurality of plans, each plan consisting of a unique subset of entities. Plans that violate at least one constraint are eliminated by the optimization engine, leaving a set of candidate solutions. An optimum plan is selected from the set of candidate solutions based on maximization of the objective.

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

Abstract: A system and method for model auto-selection for a prediction using an ensemble of machine learning models. The method includes: receiving historical data, the historical data including previous outcomes of a plurality of events associated with a plurality of data categories; training candidate machine learning models with the historical data, each candidate machine learning model trained using a respective one of the data categories; and determining an ensemble of machine learning models by determining a median prediction for combinations of candidate machine learning models and determining the combination that has the median prediction that is closest to at least one of the previous outcomes.

Abstract: A system and method for generation of automated forecasts for a subject based on one or more input parameters. The subject located at an end node of a hierarchy. The method includes: receiving historical data associated with the subject; determining the sufficiency of the historical data based on a feasibility of building a machine learning model to generate a forecast with a predetermined level of accuracy using the historical data; building the machine learning model using the historical data when there is sufficiency of the historical data; building the machine learning model using historical data associated with an ancestor node on the hierarchy when there is not sufficiency of the historical data; generating a forecast for the subject using the machine learning model based on the one or more input parameters; and outputting the forecast.

Abstract: In social data networks, it is difficult for a computing system to automatically identify gender attributes associated with user accounts because of incorrect, incomplete or non-existent data associated with the user account profile. Therefore, a computing system is provided that retrieves user account data and related text data, and that uses classification to identify gender data. Label propagation computations based on the connections in the social data network are used to infer the gender information of many user accounts at the same time.

Abstract: In social data networks, it is difficult for a computing system to automatically identify age attributes associated with user accounts because of incorrect, incomplete or non-existent data associated with the user account profile. Therefore, a computing system is provided that retrieves user account data and related text data, and that uses classification to identify age data. Label propagation computations based on the connections in the social data network are used to infer the age information of many user accounts at the same time.

Abstract: Social media networks have become a primary source for news and opinions on topics ranging from sports to politics. Sentiment analysis is typically constrained to two classes—positive and negative. A computing system is herein described for building a multi-sentiment multi-label model for electronic data that uses emojis as class labels. The electronic messages are classified into six sentiment classes. The computing system collects and creates a large corpus of clean and processed training data with emoji-based sentiment classes using little-to-no manual intervention. A threshold-based formulation is used to assign one or two class labels (multi-label) to an electronic message. The multi-sentiment multi-label model produces a desirable cross validation accuracy.

Abstract: In social data networks, it is difficult for a computing system to automatically identify demographic attributes associated with user accounts because of incorrect, incomplete or non-existent data associated with the user account profile. Therefore, a computing system is provided that retrieves user account data and related text data, and that uses Deep Learning computations to infer demographic attributes about a given user based on the text data that they generate. The text is processed, and then inputted into a bi-gram neural network to generate an initial feature vector. This initial feature vector is inputted into a Deep Learning neural network in order to generate a secondary feature vector. The secondary feature vector is inputted into a forward neural network to generate one or more values indicating a specific demographic attribute associated with the given user account.

Abstract: Determining a location of a user on a social network platform is difficult due to incorrect information or lack of information associated with the user. A system and method are provided to compute contextual similarity. This includes, for example, computing content similarity between seed users and followers/friends, as well as computing an engagement score between seed users and followers/friends. The system also computes geo-social-spatial similarity. The similarity scores are used in any inference computation to infer the geo-locations of the followers of the seed users, and subject users who share common friends with the seed users. The user geo-location inference database is updated using the result. Other seed users are selected, and the process is repeated.

Abstract: Data infrastructure systems and methods are provided for updating a continuously evolving social network. The social network is representable by a social network graph. A client application and graph data are retrieved from a database that stores the social network graph, in order to determine current activity in the social network. The current activity is used to determine one or more priority nodes in the social network graph to be updated. Social network updates are obtained for each of the one or more priority nodes. The one or more priority nodes in the social network graph are updated using the social network updates.

Abstract: A system and a method are provided for continuously analysing and procuring advertisements. Social data is obtained and is used to identify one or more relationships. The operations further include: modifying or determining a target set based on the one or more relationships, the target set comprising a combination of inputs and a target audience, the inputs comprising a search algorithm for identifying the target audience; presenting the target set when a proposed advertising campaign is detected; procuring the proposed advertising campaign using the target audience to generate a procured advertisement; obtaining feedback about the procured advertisement; and further modifying the target set based on the feedback.

Abstract: System and methods performed by a server for determining a target group of users in a social data network, including: obtaining identities of friends from an initial group of users, where a user in the group follows one or more of the friends; determining N number friends that are most frequently occurring amongst the identities of friends from the initial group; for each of the N number friends, obtaining identities of followers following a given one of the N friends; filtering out one or more followers from the identities of the followers that follow less than X number of the N number of friends, where X?N; and including remaining ones of the identities of the followers in the target group of users

Abstract: System and methods performed by a server for determining weighted influence in social networks, including: determining posts related to the topic; characterizing each post as one or more of: a reply post, a mention post, and a re-posting; generating a group of user accounts comprising any user account that authored a posting to which is replied, being mentioned in the mention post, that posted content being re-posted, and/or that authored one or more posts that are related to the topic; representing each of the user accounts as a node in the group in a connected graph and establishing an edge between one or more pair of nodes when there is a follower-followee relationship between the nodes; and for each edge between nodes, determining a weighting that is a function of one or more of: a number of mention posts, a number of reply posts, and a number of re-posts.

Abstract: System and methods performed by a server for determining weighted influence in social networks, including: determining posts related to the topic; characterizing each post as one or more of: a reply post, a mention post, and a re-posting; generating a group of user accounts comprising any user account that authored a posting to which is replied, being mentioned in the mention post, that posted content being re-posted, and/or that authored one or more posts that are related to the topic; representing each of the user accounts as a node in the group in a connected graph and establishing an edge between one or more pair of nodes when there is a follower-followee relationship between the nodes; and for each edge between nodes, determining a weighting that is a function of one or more of: a number of mention posts, a number of reply posts, and a number of re-posts.

Abstract: A method performed by a computing system is provided for searching for text sources including temporally-ordered data objects based on at least influence of an author. Users associated with a topic are identified, including authors. The users are modeled as a node and the method includes computing a topic network graph using the users as nodes and their relationships as edges. Users are ranked within the topic network graph. A search query based on a term and a time interval, including the topic, is obtained. Data objects based on the search query are identified. The method further includes: generating a popularity curve based on the frequency of data objects; identifying popular data objects based on the popularity curve; identifying an author of each of the popular data objects; and ranking the popular data objects according to a respective ranking of a respective author of each of the popular data objects.

Abstract: A system and method are provided for obtaining and analysing social data. The obtained social data and the determined relationships can be used to compose new social data and determine transmission parameters of the new social data. A method performed by a computing device or server system includes obtaining social data from one or more data streams, filtering the social data to obtain filtered social data, analysing the filtered social data to determine one or more relationships, and outputting the filtered social data and the one or more relationships in association with each other.