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.

翻译：近年来，随着基于神经网络的信息检索方法的发展以及个性化在众多搜索场景中的重要性，搜索结果个性化受到了越来越多的关注。近期研究提出在查询时利用注意力机制构建用户模型，该机制能够根据当前查询对用户相关信息的重要性进行加权。这种方法通过赋予与用户当前搜索行为更相关的信息更高权重，从而兼顾用户兴趣的多样性。本文首先探讨了标准注意力机制在个性化应用中存在的若干缺陷，尤其关注其归一化机制的问题以及无法完全滤除噪声用户相关信息的局限性。随后，我们提出去噪注意力机制（Denoising Attention mechanism）：一种通过采用鲁棒归一化方案并引入过滤机制直接解决上述缺陷的注意力变体。实验评估结果表明，该方法相较于其他基于注意力的变体具有显著优势。