发明名称 SYSTEM AND APPARATUS FOR AN APPLICATION AGNOSTIC USER SEARCH ENGINE
摘要 The system relates to a system and apparatus for an application agnostic user search engine. The system searches and retrieves users that best match a query of specified criteria. The framework is built to simultaneously support multiple applications with vastly different optimization criteria. The system may be pluggable into different commodity frameworks.
申请公布号 US2016203221(A1) 申请公布日期 2016.07.14
申请号 US201514852965 申请日期 2015.09.14
申请人 Lithium Technologies, Inc. 发明人 Rao Adithya;Spasojevic Nemanja;Oliveira Felipe;Ragtah Gaurav;Chen Jieren;Ross David
分类号 G06F17/30;G06N99/00 主分类号 G06F17/30
代理机构 代理人
主权项 1. A computer implemented system for an application agnostic user search engine for searching social networks across a plurality of computer-based social networks and external data bases sources that can be used to support applications with different optimization criteria, the system comprising: a computer data store containing: a plurality of external data base sources containing social network user data for a social network user;user profiles extracted from multiple social networks and from the external data base sources for the social network user; a computer server coupled to the computer data store and programmed to: using a collection framework for collecting social network user data from the social networks selected from the group consisting of user messages, user connections, user interactions, user biographies, user profile information, a user social graph, and user content actions and user content reactions;using a data processing component, aggregating the collected social network user data,deriving properties selected from the group consisting of topical, temporal and contextual properties from the social network user data;identifying the social network users and using the user data to create normalized user documents, the normalized user documents having demographic information, system scores, and topic interests;indexing the normalized user documents into a searchable database;identifying features in the normalized user data and using the results as training data for machine learning models;building machine learning models using historical user actions from the normalized user documents;updating the machine learning models and storing the updated machine learning models in the computer data store; andusing an application objective function and the machine learning model, outputting a list of user documents matching a query criteria.
地址 San Francisco CA US