Analysis of the Adaptive Nature of Collaborative Filtering Techniques in Dynamic Environment

Collaborative filtering (CF) has been an active area of research for a long time. However, most of the works available in the literature either focuses on handling cold start problems (when CF fails to make acceptable prediction due to the lack of ratings) or emphasizes on improving CF performance in terms of some evaluation statistics. Very few of them addressed the problem and issues of updating from a cold start affected initial stage to a steady one. To cope with this progressive nature of CF, we propose to model the entire life cycle of Recommender System (RS). Specifically, we suggest a combination of two neural network based CF techniques for the implementation of a complete RS framework. We propose to adopt the cold start based algorithm proposed by Bobadilla et al. for the initial stage. For the later stage we propose a new algorithm based on neural network. We suggest to adopt these two algorithms in different stages of CF to ensure better performance and uniformity throughout the RS life cycle.