  In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for
widespread applications such as product search and recommendation. For example,
when users search for ``running'' in e-commerce, they would like to find
products highly related to running, such as ``running shoes'' rather than
``shoes''. Nevertheless, many existing CSK collections rank statements solely
by confidence scores, and there is no information about which ones are salient
from a human perspective. In this work, we define the task of supervised
salience evaluation, where given a CSK triple, the model is required to learn
whether the triple is salient or not. In addition to formulating the new task,
we also release a new Benchmark dataset of Salience Evaluation in E-commerce
(BSEE) and hope to promote related research on commonsense knowledge salience
evaluation. We conduct experiments in the dataset with several representative
baseline models. The experimental results show that salience evaluation is a
challenging task where models perform poorly on our evaluation set. We further
propose a simple but effective approach, PMI-tuning, which shows promise for
solving this novel problem. Code is available in
\url{https://github.com/OpenBGBenchmark/OpenBG-CSK.
