Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System

Abstract

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy. Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.