NDLI: On caption bias in interleaving experiments

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On caption bias in interleaving experiments

Content Provider	ACM Digital Library
Author	Hofmann, Katja Radlinski, Filip Behr, Fritz
Abstract	Information retrieval evaluation most often involves manually assessing the relevance of particular query-document pairs. In cases where this is difficult (such as personalized search), interleaved comparison methods are becoming increasingly common. These methods compare pairs of ranking functions based on user clicks on search results, thus better reflecting true user preferences. However, by depending on clicks, there is a potential for bias. For example, users have been previously shown to be more likely to click on results with attractive titles and snippets. An interleaving evaluation where one ranker tends to generate results that attract more clicks (without being more relevant) may thus be biased. We present an approach for detecting and compensating for this type of bias in interleaving evaluations. Introducing a new model of caption bias, we propose features that model bias based on (1) per-document effects, and (2) the (pairwise) relationships between a document and surrounding documents. We show that our model can effectively capture click behavior, with best results achieved by a model that combines both per-document and pairwise features. Applying this model to re-weight observed user clicks, we find a small overall effect on real interleaving comparisons, but also identify a case where initially detected preferences vanish after caption bias re-weighting is applied. Our results indicate that our model of caption bias is effective and can successfully identify interleaving experiments affected by caption bias.
Starting Page	115
Ending Page	124
Page Count	10
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2396780
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Evaluation Implicit feedback Interleaving
Content Type	Text
Resource Type	Article

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