Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems

TL;DR

This paper introduces DCLMDB, a novel contrastive learning framework that mitigates popularity and conformity biases in recommender systems, leading to more accurate and diverse recommendations.

Contribution

The paper presents a new causal graph-based approach and a contrastive learning framework specifically designed to address dual biases in recommender systems.

Findings

DCLMDB effectively reduces popularity and conformity biases.

It significantly improves recommendation accuracy.

It enhances diversity in recommendations.

Abstract

In recommender systems, popularity and conformity biases undermine recommender effectiveness by disproportionately favouring popular items, leading to their over-representation in recommendation lists and causing an unbalanced distribution of user-item historical data. We construct a causal graph to address both biases and describe the abstract data generation mechanism. Then, we use it as a guide to develop a novel Debiased Contrastive Learning framework for Mitigating Dual Biases, called DCLMDB. In DCLMDB, both popularity bias and conformity bias are handled in the model training process by contrastive learning to ensure that user choices and recommended items are not unduly influenced by conformity and popularity. Extensive experiments on two real-world datasets, Movielens-10M and Netflix, show that DCLMDB can effectively reduce the dual biases, as well as significantly enhance the accuracy and diversity of recommendations.