Subjective Relevance: Implications on Digital Libraries for Experts and Novices

Traditional information retrieval (IR) systems are developed based on the “best match” principle which assumes that users can specify their needs in a query and documents retrieved are relevant to users. However, this objective measure of relevance is limited as it does not consider differences in experts’ and novices’ knowledge and context. This paper presents initial work towards addressing this limitation by investigating subjective relevance (that can include topical, pertinence, situational, and motivational relevance) features that can be incorporated into digital library interfaces to help experts and novices search and judge relevance more effectively. A pilot study was conducted to elicit initial subjective relevance features from experts and novices. The paper concludes with a discussion of elicited design features and their implications for user-centered digital libraries.