Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey
This is an incremental survey paper for researchers in recommendation systems, summarizing existing methods without introducing new techniques.
The paper surveys how matrix factorization models address challenges like data sparsity and scalability in collaborative filtering algorithms for recommendation systems, providing an overview of their role as a research roadmap.
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.