Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.

对于给定的用户，个性化搜索模型依赖于她的历史行为（例如发出的查询及其点击的文档）来生成兴趣概况并相应地个性化搜索结果。在兴趣分析中，大多数现有的个性化搜索方法使用“静态”文档表示作为输入，其不随当前搜索而改变。然而，文档通常很长并且包含多条信息，静态固定长度的文档向量通常不足以表示与原始查询或当前查询相关的重要信息，并且使配置文件变得嘈杂和模糊。为了解决这个问题，我们建议构建动态且面向意图的文档表示，突出显示文档的重要部分，而不是简单地对整个文本进行编码。具体来说，我们将每个文档分为多个段落，然后分别使用原始查询和当前查询与段落进行交互。此后，我们生成两个“动态”文档表示，分别包含历史和当前用户意图的关键信息。然后，我们通过捕获这些文档表示、历史查询和当前查询之间的交互来分析兴趣。现实世界搜索日志数据集的实验结果表明，我们的模型显着优于最先进的个性化方法。