A Multi-Agent Personalized Query Refinement Approach for Academic Paper Retrieval in Big Data Environment

This paper proposes a multi-agent query refinement approach to realize personalized query expansion effective for academic paper retrieval in a Big Data environment. First, we use Hadoop as a platform to develop a formalized model to represent different types of large caches of data in order to analyze and process Big Data efficiently. Second, we use a client agent to verify user identities and monitor whether a device is ready to run a query-expanded task. We then use a query expansion agent to determine the domain that the initial query belongs to by applying a knowledgebased query expansion strategy and comprehensively considering users’ interests according to the intelligent devices they use by implementing a user-device-based query expansion strategy and a weighted query expansion strategy in order to obtain the optimized query expansion set. We compare our method with the conceptual retrieval method as well as other two lexical methods for query expansion, and we prove that our method has better average recall and average precision ratios.