Sentiment scores for a first set of text can be normalized based on a statistical measure of sentiment of a corpus of text authored by a set of authors having respective personality profiles similar to a personality profile of an author of the first set of text. The set of authors can be grouped in a first cluster based on a range of at least one metric of a personality profile. A plurality of respective sentiment scores for portions of the corpus can be generated for the first cluster. A statistical measure of the plurality of respective sentiment scores can be generated. The plurality of respective sentiment scores can be normalized based on the statistical measure. The plurality of respective normalized sentiment scores can be applied to the first set of text to generate one or more sentiment scores for the first set of text.

A disparate data source aggregation system and methods are provided which may pull or retrieve talent data or features from disparate data sources, automatically correlate the data across the different data sources, build a self-adjusting system database that captures the talent data from the disparate data sources, and lets users search, query and build model insights on the aggregated data of the system database without human intervention. A method for disparate data source aggregation may include: extracting a first feature set having a first extracted feature and a second feature set having a second extracted feature; determining, if the first extracted feature of the first feature set matches the second extracted feature of the second feature set; and aggregating the first feature set with the second feature set if the first extracted feature of the first feature set matches the second extracted feature of the second feature set.

Aspects of the present disclosure relate to building a user profile data repository. A computer accesses, from a data repository, profile data of a first entity. The computer determines that a set of information items from the accessed profile data of the first entity are associated with a target activity. The computer determines that the set of information items associated with the target activity includes a subset of information items associated with a second entity. The computer creates or edits, within the data repository, profile data of the second entity based on the subset of information items. The computer provides a digital transmission of at least a portion of the profile data of the second entity.

A search engine optimizer which works independently and in parallel with a browser and search engine supercomputer to gather, analyze, and distill input information interactively. The optimizer reorganizes the input, and providing an optimized version as an output. The optimized version of the input (e.g. output) is sent to the search engine which responds to the end user with search results. The optimizer recognizes each request as a pattern and stores the pattern in an advanced Glyph format. This permits the optimizer to identify a left and ride side check mate combination required to achieve certitude.

Methods and systems for intelligently recommending selections for a selector control are disclosed. The method includes receiving a recommendation request from a selector control client, the recommendation request comprising a search string and a unique identifier of a user interacting with a selector control; identifying user identifiers of usernames matching the search string; retrieving machine learning features corresponding to the user identifiers of usernames matching the search string; applying a machine learning model to the retrieved machine learning features to assign weights to the retrieved machine learning features; computing recommendation scores for the user identifiers based on the assigned weights to the retrieved machine learning features; ranking the user identifiers based on the recommendation scores; and forwarding a ranked list of user identifiers to the selector control client for displaying in the selector control for selection by the user interacting with the selector control.

Methods for assessing the learning style preference of one or more individuals and providing targeted educational content in response to the assessed learning style preference. In one aspect, various aspects of an individual's preferred learning style (e.g., preferred learning modality, preferred social interaction, preferred method of expression, etc.) are assessed and targeted content (such as e.g., learning style preference-specific educational content) are provided to an individual based on his/her determined preferred learning style. Moreover, as learning style preference assessment occurs over time, the effectiveness of the targeted content can be tracked and an individual users' learning style preference assessment can be readily modified in order to respond to the effectiveness measure of individual ones of the provided targeted content. Apparatus, computer-readable media and systems for implementing the learning style preference assessment and provision of targeted content are also provided.

Systems and methods described herein enable effective and accurate modeling of a set of existing data profiles, perform categorization of the data profiles in an explainable way such that actions can be taken on the information to have predictable results. The systems and methods further facilitate means to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the data profiles.

A method comprises receiving a user model from a database for a particular user while the user is creating code, the user model comprising information about the particular user. The method comprises initiating engagement with the user based on the user model and at least one knowledge base trigger, and receiving a response from the user based on the initiated engagement. The method also comprises establishing an exchange with the user based on the user response, converting the exchange into code comments, and inserting the code comments into the code. The method further comprises updating the user model based on the exchange, and storing the updated user model in the database.

Systems and methods of prioritizing retrieval and/or processing of data related to a subset of attributes based on a prediction of associated values are presented herein. In certain implementations, a request for values associated with respective first attributes may be received. Based on the request, first queries for data related to the first attributes may be performed. Based on the first queries, a first subset of data related to calculating at least some of the associated values may be received. At least some of the associated values may be predicted based on the first subset of data. Based on the prediction of the associated values, retrieval and/or processing of data related to a first subset of the first attributes may be prioritized over retrieval and/or processing of data related to one or more other subsets of the first attributes.

The system and methods described herein can classify electronic activities based on sender and recipient information. The system can determine a relationship between a sender of an electronic activity and at least one recipient of the electronic activity using a sender node profile and a recipient node profile. The system can assign a tag to the electronic activity based on the relationship between the sender and one or more recipients of the electronic activity. The system can process the electronic activity based on the assigned tag.