Nowadays, we have a huge amount of information overload over Internet. To extract useful information, filtering is required. Search engines help to solve this problem to some extent but they do not deliver customized (personalized) information. Hence, there is a need for effective recommendation tools. This line of investigation leads to our central specific question for the present project. We rephrase Turing’s dictum in the following question. Can recommendation systems think? Answers to this question will have strong implications for the more general issue of whether and how far modern systems mirror our thinking. This observation has motivated us to investigate whether the collective knowledge of the community as a whole could be used to help computer systems extend their intelligent abilities.
Our common project focuses on various learning methods used in generating recommendation models and evaluation metrics used in measuring the quality and performance of recommendation algorithms. This knowledge will empower researchers from both universities and serve as a road map to improve the state of the art recommendation techniques. A combination of different complimentary methods is likely to give a more robust performance to the systems.