An adaptive algorithm for improving recommendation quality of e-recommendation systems

Nowadays, recommendation systems in e-commerce are booming because of their potential and applicability for personalized services to customers. However, recommendation is not always what customers are expecting. Odd pitches and poor matches in the system have led to outpouring of anecdotes. It means that quality control doesn't apply to the recommendation here. To cope with this problem, this paper proposes a new way to improve both the quality of rating and recommendation itself for e-recommendation system. The concepts will integrate to the implementation of our on-going e-recommendation system and the second concept is illustrated in a financial domain.