Recent years have witnessed the great successes of embedding-based methods in
recommender systems. Despite their decent performance, we argue one potential
limitation of these methods -- the embedding magnitude has not been explicitly
modulated, which may aggravate popularity bias and training instability,
hindering the model from making a good recommendation. It motivates us to
leverage the embedding normalization in recommendation. By normalizing
user/item embeddings to a specific value, we empirically observe impressive
performance gains (9\% on average) on four real-world datasets. Although
encouraging, we also reveal a serious limitation when applying normalization in
recommendation -- the performance is highly sensitive to the choice of the
temperature $\tau$ which controls the scale of the normalized embeddings.
To fully foster the merits of the normalization while circumvent its
limitation, this work studied on how to adaptively set the proper $\tau$.
Towards this end, we first make a comprehensive analyses of $\tau$ to fully
understand its role on recommendation. We then accordingly develop an adaptive
fine-grained strategy Adap-$\tau$ for the temperature with satisfying four
desirable properties including adaptivity, personalized, efficiency and
model-agnostic. Extensive experiments have been conducted to validate the
effectiveness of the proposal. The code is available at
https://github.com/junkangwu/Adap_tau.

本文研究了通过使用嵌入归一化来消除推荐系统中存在的流行偏差和训练不稳定性；通过对四个真实世界数据集的实验证明，将用户 / 项目嵌入归一化到一个特定的值可以显着提高性能。但是，这种方法高度依赖于温度 tau 的选择，因此本文还提出了一种自适应的方法来设置合适的 tau 参数，以达到更好的性能。