RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement

TL;DR

This paper introduces RankMat, a novel matrix factorization method inspired by power law and GloVe, which improves accuracy and fairness in recommender systems by leveraging Pareto distribution for modeling.

Contribution

RankMat is a new matrix factorization approach that incorporates Pareto distribution, enhancing explainability, fairness, and performance over traditional methods.

Findings

Outperforms vanilla matrix factorization in accuracy.

Comparable to GloVe-based models in fairness metrics.

Easy to implement and theoretically explainable.

Abstract

Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years, distributed word embeddings have inspired innovation in the area of recommender systems. Word2vec and GloVe have been especially emphasized in many industrial application scenario such as Xiaomi's recommender system. In this paper, we propose a new matrix factorization inspired by the theory of power law and GloVe. Instead of the exponential nature of GloVe model, we take advantage of Pareto Distribution to model our loss function. Our method is explainable in theory and easy-to-implement in practice. In the experiment section, we prove our approach is superior to vanilla matrix factorization technique and comparable with GloVe-based model in both accuracy and fairness metrics.