IDFqi

IDFqi is a proposed metric in information retrieval and text analytics that combines the traditional inverse document frequency (IDF) with a document quality component qi to produce a weighted term significance score. The aim is to adjust term importance by considering not just how rare a term is across a corpus, but also the quality of the sources in which it appears.

In practice, IDFqi can be computed in several ways because qi signals vary. A common formulation is IDFqi(t) = IDF(t) × Q(t), where Q(t) is an aggregate quality score derived from the documents that contain term t. Each document i contributing to t provides a qi value reflecting factors such as source credibility, editorial status, recency, or user-rated quality. Q(t) might be computed as an average, sum, or weighted average of these qi values, with document relevance or frequency used as weights. Alternative formulations may weight qi differently or use per-document versus per-term aggregation.

Applications of IDFqi include improving search ranking, document classification, topic modeling, and information extraction by incorporating quality signals into term weighting. When integrated with standard term weighting schemes or learning-to-rank models, IDFqi can help balance the informative power of rare terms against the trustworthiness of the sources in which those terms appear.

Limitations and challenges include reliance on quality signals that can be noisy or biased, lack of a universal standard for qi, and potential difficulties in cross-dataset comparability. Additional computational overhead and the need for careful calibration are common considerations. See also inverse document frequency, term weighting, and document quality measures.