An Integrated Recommender Algorithm for Rating Prediction

TL;DR

This paper proposes an integrated recommender algorithm that personalizes the combination of neighborhood-based collaborative filtering and matrix factorization, leading to improved rating prediction accuracy.

Contribution

It introduces a novel method that assigns personalized weights to combine two popular recommender techniques, enhancing prediction performance over fixed-weight approaches.

Findings

Outperforms neighborhood-based collaborative filtering

Outperforms matrix factorization

Outperforms fixed-weight combination methods

Abstract

Recommender system is currently widely used in many e-commerce systems, such as Amazon, eBay, and so on. It aims to help users to find items which they may be interested in. In literature, neighborhood-based collaborative filtering and matrix factorization are two common methods used in recommender systems. In this paper, we combine these two methods with personalized weights on them. Rather than using fixed weights for these two methods, we assume each user has her/his own preference over them. Our results shows that our algorithm outperforms neighborhood-based collaborative filtering algorithm, matrix factorization algorithm and their combination with fixed weights.