FOOD RECIPE RECOMMENDATION SYSTEM

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.