Abstract
Recommendation systems are most commonly used to recommend items for web users. It assists users in the selection of product from millions of product. E-Commerce websites such as AMAZON recommend items to its customers. The recommendation system mainly depends upon the previous history of its users. In this paper, a new User Rating Prediction (URP) algorithm is proposed to predict ratings for items. The proposed URP algorithm mainly depends upon similarity of users and assumes that users with similar taste may be interested in similar items. The proposed system first makes a list of related users for every user and then uses this information to predict ratings for different items. The result of the proposed algorithm was compared with the previous existing methods. The proposed algorithm gives small value of Mean Absolute Error (MAE) and root-mean-square error (RMSE) as compared to other methods.
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