Term weighting schemes are commonly used in information retrieval field to extract the most relevant terms of documents. The main contribution of this paper consists in defining a new term weighting scheme based on entropy. We believe that this scheme is particularly well adapted to compare queries from e-commerce sites. These queries have their own specificities. They tend to be short and a large proportion of them are unique queries, i.e. have no historical record. We claim that widely used weighting schemes, such as tf-idf, are not well-adapted to this kind of queries. This claim is backed up by numerical experiments where the proposed entropy-based approach is incorporated into a collabora-tive filtering framework. In this framework, well suited to e-commerce search engines, we found out, on real e-commerce purchase data, that the proposed weighting scheme outperforms the tf-idf weighting scheme.