Improving the Accuracy of M-distance Based Nearest Neighbor Recommendation System by Using Ratings Variance
Narges Hasanzadeh, Yahya Forghani · Ingénierie des systèmes d information · 2019
M-distance based recommendation system (MBR) is a nearest neighbor based recommendation method which uses the average of ratings given to an item as the attribute of that item.This attribute is used to determine similar items.Then, the average of the rating given to the similar items to an item of the active user determines the rating of that item.In this paper, to decrease the error of MBR, by combining the following ideas, eight MBR-based recommendation systems are proposed: (a) Using the variance of item ratings in addition to the average of item ratings, as two attributes of an item, for determining similar items in an item-based nearest neighbor method; (b) Using the variance of user ratings in addition to the average of user ratings, as two attributes of a user, for determining similar users in a user-based nearest neighbor method; (c) Using a weighted average method for combining the ratings of similar items or similar users; (d) Using ensemble learning.Experimental results on real datasets show that our proposed EVMBR and EWVMBR which use ensemble learning have the least error.The error of the suggested EWVMBR is at-least 20% lower than that of MBR, Slope-One, P-kNN, and C-kNN.