An Integrated Recommender Algorithm for Rating Prediction
This is an incremental improvement for e-commerce platforms like Amazon and eBay, aiming to enhance user experience by providing more accurate recommendations.
The paper tackled the problem of rating prediction in recommender systems by combining neighborhood-based collaborative filtering and matrix factorization with personalized weights for each user, resulting in an algorithm that outperformed existing methods including those with fixed weights.
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.