Empirical Analysis of Variations of Matrix Factorization in Recommender Systems

Recommender systems recommend products to users. Almost all businesses utilize recommender systems to sug- gest their products to customers based on the customer's previous actions. The primary inputs for recommendation algorithms are user preferences, product descriptions, and user ratings on prod- ucts. Content-based recommendations and collaborative filtering are examples of traditional recommendation systems. One of the mathematical models frequently used in collaborative filtering is matrix factorization (MF). This work focuses on discussing five variants of MF namely Matrix Factorization, Probabilistic MF, Non-negative MF, Singular Value Decomposition (SVD), and SVD++. We empirically evaluate these MF variants on six benchmark datasets from the domains of movies, tourism, jokes, and e-commerce. MF is the least performing and SVD is the best-performing method among other MF variants in terms of Root Mean Square Error (RMSE).