提出新指标量化推荐系统对近期行为的过度依赖
Measuring Recency Bias In Sequential Recommendation Systems
- 设计可量化序列推荐中近期偏好偏倚的新指标
- 高偏倚导致推荐性能下降，缓解后各模型表现提升
- 适合关注推荐多样性与长期兴趣建模的研究者
序列推荐系统中的近期偏倚指用户会话中对近期项目过度重视的现象。这种偏倚会削弱推荐的意外性，阻碍系统捕捉用户的长期兴趣，进而引发用户流失。本文提出一种简单而有效的新型度量方法，专门用于量化近期偏倚。研究发现，该度量所揭示的高近期偏倚会显著影响推荐性能，且在所有实验模型中，缓解该偏倚均能提升推荐表现，凸显了准确测量近期偏倚的重要性。
原文摘要 · Abstract (English)
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.
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