Driving content recommendations by building a knowledge base using weak supervision and transfer learning

Abstract

With 2.2 million subscribers and two hundred million content views, Chegg is a centralized hub where students come to get help with writing, science, math, and other educational needs. In order to impact a student's learning capabilities we present personalized content to students. Student needs are unique based on their learning style, studying environment and many other factors. Most students will engage with a subset of the products and contents available at Chegg. In order to recommend personalized content to students we have developed a generalized Machine Learning Pipeline that is able to handle training data generation and model building for a wide range of problems. We generate a knowledge base with a hierarchy of concepts and associate student-generated content, such as chat-room data, equations, chemical formulae, reviews, etc with concepts in the knowledge base. Collecting training data to generate different parts of the knowledge base is a key bottleneck in developing NLP models. Employing subject matter experts to provide annotations is prohibitively expensive. Instead, we use weak supervision and active learning techniques, with tools such as snorkel[2], an open source project from Stanford, to make training data generation dramatically easier. With these methods, training data is generated by using broad stroke filters and high precision rules. The rules are modeled probabilistically to incorporate dependencies. Features are generated using transfer learning[1] from language models for classification tasks. We explored several language models and the best performance was from sentence embeddings with skip-thought vectors predicting the previous and the next sentence. The generated structured information is then used to improve product features, and enhance recommendations made to students. In this presentation I will talk about efficient methods of tagging content with categories that come from a knowledge base. Using this information we provide relevant content recommendations to students coming to Chegg for online tutoring, studying flashcards and practicing problems.