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).

Optimization of an integrated model for automatic reduction and expansion of long queries A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intraquery term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three largescale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on userxe2x80x99s interactive query term reduction and expansion.