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  • Publication . Article . Preprint . 2015 . Embargo End Date: 01 Jan 2015
    Open Access
    Chuklin, Aleksandr; de Rijke, Maarten;
    Publisher: arXiv
    Project: EC | LIMOSINE (288024), NWO | Digging archaeology data:... (2300186891), NWO | Modeling and Learning fro... (2300171779), NWO | SPuDisc: Searching Public... (2300176811), NWO | Semantic Search in E-Disc... (2300168486), EC | VOX-POL (312827)

    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. Comment: SIGIR 2014 Workshop on Gathering Efficient Assessments of Relevance

  • Publication . Article . Preprint . 2013 . Embargo End Date: 01 Jan 2013
    Open Access
    Zoghi, M.; Whiteson, S.; Munos, R.; de Rijke, M.;
    Publisher: arXiv
    Project: NWO | SPuDisc: Searching Public... (2300176811), NWO | Semantic Search in E-Disc... (2300168486), NWO | Digging archaeology data:... (2300186891), NWO | Modeling and Learning fro... (2300171779), EC | COMPLACS (270327), EC | LIMOSINE (288024), NWO | Building Rich Links to En... (2300153702)

    This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art. Comment: 13 pages, 6 figures