Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey

TL;DR

This survey reviews how matrix factorization models enhance collaborative filtering in recommendation systems by addressing challenges like data sparsity and scalability, serving as a research roadmap.

Contribution

It provides a comprehensive overview of matrix factorization models' roles in improving collaborative filtering algorithms and discusses future research directions.

Findings

Matrix factorization effectively handles data sparsity.

MF models improve scalability of CF algorithms.

The survey highlights research gaps and future prospects.

Abstract

Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering is currently most widely used approach to build Recommendation System. CF techniques uses the user behavior in form of user item ratings as their information source for prediction. There are major challenges like sparsity of rating matrix and growing nature of data which is faced by CF algorithms. These challenges are been well taken care by Matrix Factorization. In this paper we attempt to present an overview on the role of different MF model to address the challenges of CF algorithms, which can be served as a roadmap for research in this area.