中文摘要：

用户查询意图模型是查询扩展和查询推荐研究的一个热点。然而，日志包含的大量噪声对主流的用户查询意图模型构建过程具有较大负面影响，观察日志发现，用户试探性点击行为是日志噪声产生的一个主要原因，并由此提出了一种融合用户学习过程的用户查询意图模型。该模型对用户从试探性点击行为进行建模，从而对试探性点击行为进行识别和过滤。一系列实验结果表明，该模型在日志噪声较高的情况下能够有效过滤试探性点击产生的噪声，提高用户查询意图描述的准确率；将该模型应用于查询推荐过程后，能有效提高查询条件相似性计算的准确率，并提高查询推荐结果的准确率和召回率。

英文摘要：

User intent modeling is a hot point in researches of query expansion and query recommendation. But the large amount of noise in search log have great negative impact on the construction of user intent model. By observing the log it can be found that tentative click of the user is one of the main causations of irrelevant feedbacks. To solve the problem, this paper proposed the user intent model combined with studying process. It modeled the tentative clicks, and then identified and filtered those tentative clicks. Test results show that the model can effectively filter noises coming from tentative clicks when log owns high level noisy. After applying the model to query recommendation it can improve the precision of similarity computation among queries, and increases the precision of query recommendation obviously.