Explanation of relevance judgement discrepancy with quantum interference A key concept in many Information Retrieval (IR)  :8,document indexing, query language modelling,  :14,diversity retrieval, is the relevance  :20,44,topics, i.e. to what extent an information  :28,a :8,document or :28,a query) is about the topics.  :38,investigates the interference of relevance  :28,a topic caused by another topic. For  :52,that two user groups are required  :59,whether :28,a topic q is relevant to :28,a :8,document  q is :69,presented together with another topic  :77,as :28,a companion topic). If different companion  :85,used for different groups, interestingly different  of q :92,given d can be reached. In  :101,we present empirical results showing :52,that the  :28,a topic to :28,a :8,document is :109,greatly affected by  :119,topicxe2x80x99s relevance to the same document,  extent of the :126,impact differs with respect to  topics. :135,We further analyse the  :141,classical and quantumlike interference  :146,connect the phenomenon to nonreality  in :152,quantum mechanics. :135,We  :152,quantum :157,like model fits in the empirical data,  :166,potentially :85,used for predicting the relevance when interference exists.

Comparing the Effectiveness of QueryDocument Clusterings Using the QDSM and Cosine Similarity Typically, approaches based on query clustering in IR only take the keywords that belong to the queries into consideration, with the aim of calculating the similarity among them. In search engines, one of the factors that affects precision is the short lengths of queries. One way to tackle this problem is through the use of the context. Aiming to consider the context of queries, several measures have been proposed to calculate their interdocument similarity, which are known as QuerySensitive Similarity Measure (QSSM). This paper presents a new QSSM called QueryDocument Similarity Measure (QDSM), which is used to build clusters of similar search results. The goal of this measure is to provide clusters of better quality (i.e., clusters with more relevant documents) taking into account the context in which the queries are submitted. To achieve this goal, several experiments were carried out using four wellknown algorithms in Information Retrieval (IR) along with the AOL Query Logs Dataset (AOL). Final results using Bisection KMeans and Average link show that the clusters built with QDSM provide better effectiveness than the clusters built with Cosine Similarity (Sc).