In the first part of this thesis we investigate how user interactions with search engines can be used to evaluate search engines. In particular, we introduce a new online evaluation paradigm called multileaving that extends upon interleaving. With this new method, fewer users need to be exposed to the results from possibly inferior search engines.
In the second part of this thesis we turn to online learning to rank. We learn from the evaluation methods introduced and extended upon in the first part. The important implication is that search engines can adapt more quickly to changes in user preferences.
In the last part we introduce a new shared resource and a new evaluation paradigm. Lerot is an online evaluation framework that allows us to simulate users interacting with a search engine. Secondly we introduce OpenSearch, a new evaluation paradigm involving real users of real search engines.
Series: SIKS dissertation series 2016-11
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