Analysis and Evaluation of Recommendation System Algorithms
As the diffusion of e- commerce sites i.e. amazon, flipcart, ebay etc. users contribute comments, reviews, ratings and feedbacks actively in these e-commerce sites which contains massive amount of information online. Recommendation has gained attraction from more researchers to narrow the search space on searching the specific product for a user by providing personalization and filtering systems. Recommendation Engine uses analytic methods which generate meaningful recommendations to a group of users for items i.e. movies, products, songs, books etc. that might interest them. There are various methods defined for recommending items to users but they all have its merits and demerits, based on which dataset we use, type of users and for which item we have to provide recommendation they are adopted. The purpose of this paper is to review the different recommendation techniques that are efficient and used for Recommend most suitable items to users for their needs. Various issues of recommendation techniques are listed and performance analysis of different methods is given.
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