The Anatomy of Relevance: Topical, Snippet and Perceived Relevance in Search Result Evaluation
This work addresses the issue of more accurate search result evaluation for users and developers, but it is incremental as it builds on existing relevance concepts.
The paper tackles the problem of evaluating search engine quality by highlighting that current methods focus only on topical relevance, and it proposes incorporating perceived relevance and snippet relevance to better assess user utility, though no concrete numerical results are provided.
Currently, the quality of a search engine is often determined using so-called topical relevance, i.e., the match between the user intent (expressed as a query) and the content of the document. In this work we want to draw attention to two aspects of retrieval system performance affected by the presentation of results: result attractiveness ("perceived relevance") and immediate usefulness of the snippets ("snippet relevance"). Perceived relevance may influence discoverability of good topical documents and seemingly better rankings may in fact be less useful to the user if good-looking snippets lead to irrelevant documents or vice-versa. And result items on a search engine result page (SERP) with high snippet relevance may add towards the total utility gained by the user even without the need to click those items. We start by motivating the need to collect different aspects of relevance (topical, perceived and snippet relevances) and how these aspects can improve evaluation measures. We then discuss possible ways to collect these relevance aspects using crowdsourcing and the challenges arising from that.