As a mechanism to guide users towards a better representation of their information needs, the query reformulation method generates new queries based on users’ historical queries. To preserve the original search intent, query reformulations should be context-aware and should attempt to meet users’ personal information needs. The mainstream method aims to generate candidate queries first, according to their past frequencies, and then score (re-rank) these candidates based on the semantic consistency of terms, dependency among latent semantic topics and user preferences. We exploit embeddings (i.e. term, user and topic embeddings) to use contextual information and individual preferences more effectively to improve personalised query reformulation. Our work involves two major tasks. In the first task, candidate queries are generated from an original query by substituting or adding one term, and the contextual similarities between the terms are calculated based on the term embeddings and augmented with user personalisation. In the second task, the candidate queries generated in the first task are evaluated and scored (re-ranked) according to the consistency of the semantic meaning of the candidate query and the user preferences based on a graphical model with the term, user and topic embeddings. Experiments show that our proposed model yields significant improvements compared with the current state-of-the-art methods.

作为引导用户更好地表示其信息需求的机制，查询重构方法根据用户的历史查询生成新的查询。为了保留原始搜索意图，查询重构应该是上下文感知的，并且应该尝试满足用户的个人信息需求。主流方法旨在首先根据候选查询的过去频率生成候选查询，然后根据术语的语义一致性、潜在语义主题之间的依赖性和用户偏好对这些候选查询进行评分（重新排序）。我们利用嵌入（即术语、用户和主题嵌入）来更有效地使用上下文信息和个人偏好来改进个性化查询重构。我们的工作包括两大任务。在第一个任务中，候选查询是通过替换或添加一个术语从原始查询生成的，并且术语之间的上下文相似度是基于术语嵌入计算的，并通过用户个性化进行增强。在第二个任务中，基于具有术语、用户和主题的图形模型，根据候选查询的语义和用户偏好的一致性，对第一个任务中生成的候选查询进行评估和评分（重新排序）嵌入。实验表明，与当前最先进的方法相比，我们提出的模型产生了显着的改进。根据具有术语、用户和主题嵌入的图形模型，根据候选查询的语义和用户偏好的一致性，对第一个任务中生成的候选查询进行评估和评分（重新排序）。实验表明，与当前最先进的方法相比，我们提出的模型产生了显着的改进。根据候选查询语义的一致性和用户偏好，基于具有术语、用户和主题嵌入的图形模型，对第一个任务中生成的候选查询进行评估和评分（重新排序）。实验表明，与当前最先进的方法相比，我们提出的模型产生了显着的改进。