Abstract: Techniques for performing a database search using a rewritten and annotated query are disclosed herein. In example embodiments, a profile lexicon is generated from a set of raw user profiles. A click-through lexicon is generated from a raw query log. A machine-learning model is trained for entity prediction using selected data. Query tagger data is generated using the profile lexicon, the click-through lexicon, and the machine-learning model. A raw query is received. The raw query is rewritten as an annotated query based on the generated query tagger data. A search of a database is performed using the annotated query. Results of the search are returned in response to the receiving of the raw query for presentation in a user interface.

Abstract: Techniques for inferring a specific skill associated with a job posting are described. In an example, disclosed is a system that selects, from a jobs database, a specific job posting from a plurality of job postings. Additionally, job applicants for the specific job posting can be determined using indicators in the profile data of members. Moreover, a set of skills associated with the job applicants can be obtained. Furthermore, a percentage of the job applicants having a specific skill from the set of skills can be determined using the profile data of the job applicants. Subsequently, a confidence score of the specific skill being associated with the specific job posting can be calculated based on the percentage of the job applicants having the specific skill. A user interface can display a presentation of the specific job posting to a first member when the confidence score transgresses a predetermined score.

Abstract: Techniques for performing a database search using a rewritten and annotated query are disclosed herein. In example embodiments, a profile lexicon is generated from a set of raw user profiles. A click-through lexicon is generated from a raw query log. A machine-learning model is trained for entity prediction using selected data. Query tagger data is generated using the profile lexicon, the click-through lexicon, and the machine-learning model. A raw query is received. The raw query is rewritten as an annotated query based on the generated query tagger data. A search of a database is performed using the annotated query. Results of the search are returned in response to the receiving of the raw query for presentation in a user interface.

Abstract: Techniques for learning and leveraging embeddings for response prediction are provided. Based on training data, an embedding for each attribute value of multiple content items is generated, an embedding for each attribute value of multiple entities is generated, weights of a first neural network for content items is generated, and weights of a second neural network for requesting entities is generated. In response to receiving a request, a particular content item is identified. A first set of embeddings for the particular content item is identified and input into the first neural network to generate first output. A particular requesting entity that initiated the content request is identified. A second set of embeddings for the particular requesting entity is identified and input into the second neural network to generate second output. The particular content item is selected based on the first output and the second output.

Abstract: Systems and methods for identifying appropriate course recommendations are disclosed. A system receives a request for recommended courses and accesses a plurality of course records to determine whether a sufficient number of courses that teach the skill of interest are available. In accordance with a determination that a sufficient number of courses that teach the skill of interest are not available, the system generates skill attribute vectors for a skill of interest and a plurality of other skills and ranks the plurality of other skills based on the distance between the skill attribute for the skill of interest and the plurality of other skills. The system selects a skill based on the rankings. The system identifies at least one course that teaches the selected skill and transmits a course recommendation for the identified course to a client system for presentation.

Abstract: Techniques for inferring a specific skill associated with a job posting are described. In an example, disclosed is a system that selects, from a jobs database, a specific job posting from a plurality of job postings. Additionally, job applicants for the specific job posting can be determined using indicators in the profile data of members. Moreover, a set of skills associated with the job applicants can be obtained. Furthermore, a percentage of the job applicants having a specific skill from the set of skills can be determined using the profile data of the job applicants. Subsequently, a confidence score of the specific skill being associated with the specific job posting can be calculated based on the percentage of the job applicants having the specific skill. A user interface can display a presentation of the specific job posting to a first member when the confidence score transgresses a predetermined score.

Abstract: Techniques for associating a specific skill with a job posting are described. In an example, disclosed is a system having a standardization database and a skills database. The system can extract textual features from a job posting, and select a specific skill for the job posting from a plurality of skills. Moreover, a job title for the job posting can be selected from a plurality of standardized job titles. Furthermore, an affinity score can be accessed for specific skill based on the selected job title. The system can determine a skill frequency score associated with the specific skill. The system can calculate a confidence score of the specific skill based on the textual features, the affinity score, and the skill frequency score. Subsequently, the system can store, in the skills database, an association of the specific skill with the job posting when the confidence score is above a predetermined threshold.

Abstract: A system, method and computer program product for multi-player video gaming or chatting, including a virtual representation of a user, wherein the virtual representation of the user is configured for being displayed and used on a computer device in place of the user during online and/or offline video gaming, chatting, and/or virtual social interactions.