Analysis and Performance Evaluation of
Cosine Neighbourhood Recommender System
Kola Periyasamy1,
Jayadharini Jaiganesh1, Kanchan Ponnambalam1, Jeevitha Rajasekar1,
and Kannan Arputharaj2
1Department of Information Technology, Anna
University, India.
2Department
of Information Science and Technology, Anna University, India.
Abstract: Growth of technology and innovation leads
to large and complex data which is coined as Bigdata. As the quantity of
information increases, it becomes more difficult to store and process data. The
greater problem is finding right data from these enormous data. These data are
processed to extract the required data and recommend high quality data to the
user. Recommender system analyses user preference to recommend items to user. Problem
arises when Bigdata is to be processed for Recommender system. Several
technologies are available with which big data can be processed and analyzed.
Hadoop is a framework which supports manipulation of large and varied data. In
this paper, a novel approach Cosine Neighbourhood Similarity measure is
proposed to calculate rating for items and to recommend items to user and the
performance of the recommender system is evaluated under different evaluator
which shows the proposed Similarity measure is more accurate and reliable.
Keywords: Big
Data, Recommender System, Cosine Neighbourhood Similarity, Recommender
Evaluator.
Received April 28, 2014; accepted June 12, 2014