Abstract:
Since the evaluation of World wide web from social networks to ecommerce the goal of
every system is to get more business. In last few decades online systems use timestamp for
recommendations whereas the source of data increase. Now systems use user preference for
recommendations i.e., Collaborative Recommendations. The technique of collaborative
filtering is especially successful in generating personalized recommendations. More than a
decade of research has resulted in numerous algorithms, although no comparison of the
different strategies has been made. In fact, a universally accepted way of evaluating a
collaborative filtering algorithm does not exist yet. In this work, we compare different
techniques found in the literature, and we study the characteristics of each one, highlighting
their principal strengths and weaknesses. Several experiments have been performed, using the
most popular metrics and algorithms. Moreover, two new metrics designed to measure the
precision on good items have been proposed. The results have revealed the weaknesses of
many algorithms in extracting information from user profiles especially under sparsity
conditions.
