2015/01/26 by Aleksandr Chuklin, Chuklin, Aleksandr, Maarten de Rijke +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #H.3.3 #Information Retrieval (cs.IR) #Information Retrieval and Search Behavior #Topic Modeling #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.1501.06412
openalex publication_date 2015/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.