Intelligent Chatbot-LDA Recommender System

Abstract

With the proliferation of distance platforms, in particular that of an open access such as Massive Online Open Courses (MOOC), the learner finds himself overwhelmed with data which are not all efficient for his interest. Besides, the MOOC has tools that allow learners to seek information, express their ideas, and participate in discussions in an online forum. This tool is a huge repository of rich data, which continues to evolve, however its exploitation is fiddly in the search for information relevant to the learner. Similarly, the task of the tutor seems to be difficult in management of a large number of learners. To this end, the development of a Chatbot able to meet the requests of learners in a natural language is necessary to the deroulement a course in the MOOC. The ChatBot plays the role of assistant and guide for the learners and for the tutors. However, ChatBot responses come from a knowledge base, which must be relevant. Knowledge extraction to answer questions is a difficult task due to the number of MOOC participants. Learners' interactions with the MOOC platform gen-erate massive information, particularly in discussion forums by seeking answers to their questions. Identifying and extracting knowledge from online forums requires collaborative interactions between learners. In this article we propose a new approach to answer learners' questions in a relevant and instantaneous way in a ChatBot in natural language. Our model is based on the LDA Bayesian statistical method, applied to threads posted in the forum and classifies them to provide the learner with a rich semantic response. These threads taken from the discussion forum in the form of knowledge will enrich the ChatBot knowledge database. In parallel, we will map the extracted knowledge to ontology, to provide the learner with pedagogical resources that will serve as learning support.