  The personalization of search results has gained increasing attention in the
past few years, thanks to the development of Neural Networks-based approaches
for Information Retrieval and the importance of personalization in many search
scenarios. Recent works have proposed to build user models at query time by
leveraging the Attention mechanism, which allows weighing the contribution of
the user-related information w.r.t. the current query. This approach allows
taking into account the diversity of the user's interests by giving more
importance to those related to the current search performed by the user.
  In this paper, we first discuss some shortcomings of the standard Attention
formulation when employed for personalization. In particular, we focus on
issues related to its normalization mechanism and its inability to entirely
filter out noisy user-related information. Then, we introduce the Denoising
Attention mechanism: an Attention variant that directly tackles the above
shortcomings by adopting a robust normalization scheme and introducing a
filtering mechanism. The reported experimental evaluation shows the benefits of
the proposed approach over other Attention-based variants.
