Popularity bias is a widespread problem in the field of recommender systems, where popular items tend to dominate recommendation results. In this work, we propose 'Test Time Embedding Normalization' as a simple yet effective strategy for mitigating popularity bias, which surpasses the performance of the previous mitigation approaches by a significant margin. Our approach utilizes the normalized item embedding during the inference stage to control the influence of embedding magnitude, which is highly correlated with item popularity. Through extensive experiments, we show that our method combined with the sampled softmax loss effectively reduces popularity bias compare to previous approaches for bias mitigation. We further investigate the relationship between user and item embeddings and find that the angular similarity between embeddings distinguishes preferable and non-preferable items regardless of their popularity. The analysis explains the mechanism behind the success of our approach in eliminating the impact of popularity bias. Our code is available at https://github.com/ml-postech/TTEN.

翻译：流行度偏差是推荐系统领域中一个普遍存在的问题，热门物品往往主导推荐结果。在本文中，我们提出“测试时嵌入归一化”作为一种简单而有效的流行度偏差缓解策略，其性能显著超过了以往的缓解方法。我们的方法在推理阶段利用归一化后的物品嵌入来控制嵌入幅值的影响，而该幅值与物品流行度高度相关。通过大量实验，我们展示了将本方法与采样softmax损失相结合，相较于以往的偏差缓解方法能更有效地降低流行度偏差。我们进一步研究了用户嵌入与物品嵌入之间的关系，发现嵌入之间的角度相似性能够区分用户偏好与非偏好的物品，且不受其流行度影响。该分析解释了本方法在消除流行度偏差影响方面取得成功的机制。我们的代码见https://github.com/ml-postech/TTEN。