Web search has become a popular way for people to obtain information. Due to the complexity of search task, one retrieval cannot serve all of user’s information needs. Multiple interactions with the search engine are required, and session search comes into being. Currently proposed session search methods still cannot take advantage of interactive information to capture the user’s implicit information needs. Recently, quantum theory has been successively applied into information retrieval task. We find the similarities between quantum measurements and session interactions. This paper thus develops a Quantum Interactive Retrieval Model (QIRM), which involves both quantum standard measurement (QSM) and quantum weak measurement (QWM) to re-characterize the user’s cognition shifts during a session interaction. Particularly, the user’s cognition states in strong interaction and weak interaction are quantified into quantum-like representations formalized by QSM and QWM, respectively. The representations are then viewed as the user’s information needs for computing ranking score of an evaluated document. We conduct experiments on TREC 2013 and 2014 Session Track datasets. The empirical evaluation demonstrates the effectiveness of our proposed methods, which obtains very comparable performance in terms of nDCG@10 and ERR@10.

网络搜索已成为人们获取信息的一种流行方式。由于搜索任务的复杂性，一次检索无法满足用户的所有信息需求。需要与搜索引擎进行多次交互，并且会话搜索应运而生。当前提出的会话搜索方法仍然不能利用交互式信息来捕获用户的隐式信息需求。近来，量子理论已被相继应用于信息检索任务。我们发现量子测量和会话相互作用之间的相似之处。因此，本文开发了一种量子交互式检索模型（QIRM），该模型同时涉及量子标准测量（QSM）和量子弱测量（QWM），以重新表征会话交互过程中用户的认知变化。特别，用户在强相互作用和弱相互作用中的认知状态分别量化为由QSM和QWM形式化的量子状表示。然后，将表示形式视为用户的信息需求，以计算评估文档的排名得分。我们对TREC 2013和2014 Session Track数据集进行实验。经验评估证明了我们提出的方法的有效性，该方法在nDCG @ 10和ERR @ 10方面获得了非常可比的性能。