  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.
