AQtpUIR: Adaptive query term proximity based user information retrieval
Tirthankar Barik, Vikram Singh · Journal of Information and Optimization Sciences · 2020
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.