Measuring Recency Bias In Sequential Recommendation Systems

TL;DR

This paper introduces a new metric to quantify recency bias in sequential recommendation systems, showing that high recency bias negatively affects performance and that mitigation improves recommendation quality.

Contribution

The paper proposes a novel metric for measuring recency bias and demonstrates its impact on recommendation performance across various models.

Findings

High recency bias correlates with poorer recommendation performance.

Mitigating recency bias improves recommendation accuracy.

The proposed metric effectively quantifies recency bias in systems.

Abstract

Recency bias in a sequential recommendation system refers to the overly high emphasis placed on recent items within a user session. This bias can diminish the serendipity of recommendations and hinder the system's ability to capture users' long-term interests, leading to user disengagement. We propose a simple yet effective novel metric specifically designed to quantify recency bias. Our findings also demonstrate that high recency bias measured in our proposed metric adversely impacts recommendation performance too, and mitigating it results in improved recommendation performances across all models evaluated in our experiments, thus highlighting the importance of measuring recency bias.