Model of Recommendation System to Enhance the Employability: Machine Learning Approach

Abstract

In the recent era, various types of data are available on the Web in various forms such as text, images, audio, and video. Extracting information of interest from this web content is one of the applications of Web Content Mining. Computer Science is one of the most demanding fields where students can get the best recruitment in the IT (Information Technology) industry through job portals. Still, there is a gap of skills between academia and industry. One of the prominent factors is non-competent syllabus. This Research Paper introduces the Industry Institute Interaction Recommendation System-IIIRS model of a Web Mining technique. It is based on a recommendation system to enhance the employability of students, by bridging the gap between the institution’s curriculum and the required skills set at the industry end. The proposed model includes application of data extraction, data pre-processing, classification, and clustering. The model presents the state of the art skills required by the software industry, and gives recommendations about required updates in the syllabus of academic institute. The model serves the purpose of both industry and academia by bridging the gap of skill sets due to incompetent syllabus.