Making Mind and Machine Meet - A Study of Combining Cognitive and Algorithmic Relevance Feedback

摘要

Using Saracevic's relevance types, we explore approaches to combining algorithm and cognitive relevance in a term relevance feedback scenario. Data collected from 21 users who provided relevance feedback about terms suggested by a system for 50 TREC HARD topics are used. The former type of feedback is considered as cognitive relevance and the latter type is considered as algorithm relevance. We construct retrieval runs using these two types of relevance feedback and experiment with ways of combining them with simple Boolean operators. Results show minimal differences in performance with respect to the different techniques.