DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems

TL;DR

DotMat is a novel algorithm that addresses cold-start and sparsity issues in recommender systems without using additional data, achieving competitive results with traditional methods.

Contribution

Introduces DotMat, a new method that effectively solves cold-start and sparsity problems without relying on side information or extra input data.

Findings

DotMat achieves competitive performance with full-data matrix factorization.

It effectively alleviates cold-start and sparsity issues.

Experimental results validate its effectiveness.

Abstract

Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable amount of efforts trying to solve the problems. However, for cold-start problem, most research relies on importing side information to transfer knowledge. A notable exception is ZeroMat, which uses no extra input data. Sparsity is a lesser noticed problem. In this paper, we propose a new algorithm named DotMat that relies on no extra input data, but is capable of solving cold-start and sparsity problems. In experiments, we prove that like ZeroMat, DotMat can achieve competitive results with recommender systems with full data, such as the classic matrix factorization algorithm.