Personalized search plays an important role in satisfying users' information needs owing to its ability to build user profiles based on users' search histories. Most of the existing personalized methods built dynamic user profiles by emphasizing query-related historical behaviors rather than treating each historical behavior equally. Sometimes, the ambiguity and short nature of the query make it difficult to understand the potential query intent exactly, and the query-centric user profiles built in these cases will be biased and inaccurate. In this work, we propose to leverage candidate documents, which contain richer information than the short query text, to help understand the query intent more accurately and improve the quality of user profiles afterward. Specifically, we intend to better understand the query intent through candidate documents, so that more relevant user behaviors from history can be selected to build more accurate user profiles. Moreover, by analyzing the differences between candidate documents, we can better control the degree of personalization on the ranking of results. This controlled personalization approach is also expected to further improve the stability of personalized search as blind personalization may harm the ranking results. We conduct extensive experiments on two datasets, and the results show that our model significantly outperforms competitive baselines, which confirms the benefit of utilizing candidate documents for personalized web search.
