Towards Enhancing Information Retrieval Systems: A Brief Survey of Strategies and Challenges The exponentially increasing of resources generated and stored in various forms and made available within the internet has posed numerous challenges inside information retrieval (IR) operations and consequently accelerated a myriad of studies in the realm of IR technologies. In the IR system, the large part of the researches is centred on how to satisfy an information requirement from a query to voluminous document sets. In this regard, the importance is on improving the relevance quality of the results (i.e. retrieved documents). The effectiveness of the IR system depends on the efficacy of the respective adopted IR model and strategy. Understanding of the IR models, strategies and their challenges is important in choosing an appropriate and viable strategy toward the development of effective IR systems for a specificpredefined role. This paper surveys the basic IR models, challenges and adopted strategies to enhance the IR systems are also highlighted.

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.