Principles, techniques and evaluation of recommendation systems

Abstract

On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. Recommender systems are a computer-based method that helps the user by generating suggestions about new items and products. It does so with the help of the past ratings of the item or analysing the preferences of the user's friends in the social network. The recommender system is further optimized by considering the user demographics which further help in filtering the output. Recommender systems have a wide range of applications. It ranges from movies music, news, books, products to research articles, search queries, social tags, etc. This paper explores the different characteristics and potentials of two different prediction techniques which include Collaborative Filtering and Content-based Filtering in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.