AQtpUIR: Adaptive query term proximity based user information retrieval

Abstract

Information-seeking process primarily relies on the relevance manifestation and usually based on term statistics. Document-term statistics are dominant, e.g. term-frequency (TF), inverse document frequency (IDF), document length (DL), etc. Query Term Proximity (QTP) is mostly under-explored for the relevance estimation in the information retrieval. In this paper, we systematically review the lineage of the notion of QTP and proposed a novel framework for relevance estimation. The proposed framework is referred as Adaptive QTP based User Information Retrieval (AQtpUIR), is intended to promote the relevant documents. Here, the relevance estimation is a weighted combination of statistics. The notions ‘term-term query proximity’ is a simple aggregation of contextual aspects of user search in relevance estimates and query formation. Intuitively, QTP is derived via pre-processing, inherent to indexing and text-processing, and utilized to promote the extracted documents among all retrieved documents. Thus also balance the exploitation-exploration tradeoff. The adaption of QTP balance the traditional retrieval tradeoff, e.g. relevance, novelty, result diversity (Coverage and Topicality), and highlight various inherent challenges and issue of the proposed work.

Subject Classification:

• Information science

Keywords:

• Big data analytics
• Exploratory search
• Relevance manifestation
• Information retrieval