A recommendation model for handling dynamics in user profile

Abstract

Recommender System has become the necessary agent for a naive user in the information bombardment arena of World Wide Web. In the last decade, World Wide Web emerged as an all encompassing technology that is revolutionizing the way people live. With the passage of time, user behaviors evolve and as such should be the recommendations provided. There exists a wide gap in literature to cater effectively with the issue of temporal evolution of data on the internet. This paper will specifically analyze various ways through which temporal issue can be handled in generating user profile that evolve with time. It will develop a recommendation model to handle the dynamics in user profile. The prime focal point is to examine if recommendation accuracy can be improved by adding temporal dimension. An empirical study is carried out to compare the analysis of the traditional data mining tasks and the proposed method. © 2012 Springer-Verlag.