Design and Implementation of Semantic and Content based Hybrid Recommender System for Java Programs

Abstract

During the past few years the World Wide Web has emerged as the mainstream medium of communication and information dissemination. With the rapid growth of the WWW and the advent of eservices for online shopping, social networking, email and more; The Web personalization[10,11] and recommendation system has now become one of the most important tool for both Web-based organizations as well as for end users in order to extract the “right” and “interesting” information from the World Wide Web. Recommendation system (RS) is one of the most advanced approaches which are widely used for personalization of information on the web and information retrieval systems. Recommendation systems are now popular commercially as well as in Research community. Many major e-commerce Websites are already using recommendation systems to increase their customers by providing relevant suggestions to their customers and providing them better recommendation for purchasing of products. The recommendations could be based on various parameters, such as customer‟s behavior of purchasing, rating and commenting; user characteristics such as geographical location or other demographic information.. In this paper we are proposing the design and implementation of a computer programs recommender system that recommends the user; Java programs; which are similar to program that a user is currently interested in. In order to achieve this, we have prepared a tag list, which is a list of keywords, packages and classes available in Java that have been used to match the program similarity with each other. With each program in database, a heading is associated which is displayed before user to choose one. Feature extraction is achieved by identifying tags available in program heading as well as in contents of a program. A threshold value (t) is also available which determines how similar a program should be in order to be recommended to the user. The proposed system can work in three different modes: Heading based recommendations, Content based recommendations and Mixed recommendations.