Online Evaluation for Information Retrieval

By Katja Hofmann, Lihong Li, Filip Radlinski. 2016

115 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Masrour Zoghi
2. Ilya Markov
3. Chang Li
4. Maarten de Rijke
5. Shimon Whiteson

In Information Systems

1. Masrour Zoghi
2. Ilya Markov
3. Chang Li
4. Maarten de Rijke
5. Shimon Whiteson
6. Thorsten Joachims

Citations in the corpus

• MergeDTS (2018, background): “In the absence of an oracle judge who can tell the preferences between all rankers, the best ranker is usually inferred from user feedback on the result lists produced by the rankers [20].”
• Neural Approaches to Conversational AI (2018, unknown): “rs can be much larger, thus resulting in greater flexibility in evaluation. In this process, many online and offline evaluation techniques such as A/B-testing and counterfactual estimation can be used (Hofmann et al., 2016). The major downside of experimenting with actual users is the risk of negative”