Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document content and user query context using both lexical representations and semantic embeddings such that the user's intent can be better understood by data enrichment of personalized query context information. Extensive experiments performed on the search dataset, demonstrate the effectiveness of the proposed method.

翻译：现有神经相关性模型对查询和项目上下文信息的考虑不足，这些信息通过多样化搜索结果以适应个人偏好。为弥补这一不足，本文提出一种神经学习框架，通过利用信号捕捉文档如何适应用户上下文，实现个性化文档排序。具体而言，该框架同时使用词汇表示和语义嵌入建模文档内容与用户查询上下文之间的关系，从而通过个性化查询上下文信息的数据增强更好地理解用户意图。在搜索数据集上进行的大量实验证明了所提方法的有效性。