Measuring Recency Bias In Sequential Recommendation Systems
This addresses a specific issue in recommendation systems for improving user experience, but it is incremental as it focuses on measurement rather than a new solution.
The paper tackles the problem of recency bias in sequential recommendation systems, which overly emphasizes recent items and harms user engagement, by proposing a novel metric to quantify it; results show that mitigating this bias improves recommendation performance across all evaluated models.
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.