GreyShot: Zeroshot and Privacy-preserving Recommender System by GM(1,1) Model

TL;DR

GreyShot introduces a zero-shot, privacy-preserving recommender system leveraging GM(1,1) grey system model, effectively addressing cold-start issues without input data, and producing accurate and fair recommendations.

Contribution

The paper presents GreyShot, a novel zero-shot recommender system using GM(1,1) that operates without input data and effectively solves cold-start problems.

Findings

Effective in cold-start scenarios

Produces accurate and fair recommendations

Operates without input data

Abstract

Every recommendation engineer needs to face the cold start problem when building his system. During the past decades, most scientists adopted transfer learning and meta learning to solve the problem. Although notable exceptions such as ZeroMat etc. have been invented in recent years, cold-start problem remains a challenging problem for many researchers. In this paper, we build a zeroshot and privacy-preserving recommender system algorithm GreyShot using GM(1,1) model by taking advantage of the Poisson-Pareto property of the online rating data. Our approach relies on no input data and is effective in generating both accurate and fair results. In conclusion, zeroshot problem of recommender systems could be effectively solved by grey system methods such as GM(1,1).