  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
\url{https://github.com/junkangwu/Adap_tau}.
