Computerized system and method for applying transfer learning for generating a multi-variable based unified recommendation

Abstract

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 novel recommendation framework that automatically applies transfer learning from a knowledge-base and generating a multi-variable based unified recommendation. The framework dynamically develops a universal word dictionary between a source language and a target language based on Natural Language Processing (NLP word embeddings. This not only leverages the accuracy of multi-lingual product embeddings, but also increases the efficiency and effectiveness in how it can be applied. The disclosed systems and methods, therefore, provides a novel computerized solution for how different knowledges learnt from one electronic platform can be adapted to a global platform for providing global users electronic information.