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Distributed and Self-organizing Systems
Distributed and Self-organizing Systems

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From TMR to Turtle: Predicting Result Relevance from Mouse Cursor Interactions in Web Search


Journal Article





Sebastian Nuck

Sebastian Nuck

Lars Wesemann

Lars Wesemann

Andreas Both

Andreas Both



Journal of Web Engineering. Vol.14 No.5&6






The prime aspect of quality for search-driven web applications is to provide users with the best possible results for a given query. Thus, it is necessary to predict the relevance of results a priori. Current solutions mostly engage clicks on results for respective predictions, but research has shown that it is highly beneficial to also consider additional features of user interaction. Nowadays, such interactions are produced in steadily growing amounts by internet users. Processing these amounts calls for streaming-based approaches and incrementally updatable relevance models. We present StreamMyRelevance!—a novel streaming-based system for ensuring quality of ranking in search engines. Our approach provides a complete pipeline from collecting interactions in real-time to processing them incrementally on the server side. We conducted a large-scale evaluation with real-world data from the hotel search domain. Results show that our system yields predictions as good as those of competing state-of-the-art systems, but by design of the underlying framework at higher efficiency, robustness, and scalability.

Additionally, our system has been transferred into a real-world industry context. A modified solution called Turtle has been integrated into a new search engine for general web search. To obtain high-quality judgments for learning relevance models, it has been augmented with a novel crowdsourcing tool.

Reference Copy

Maximilian Speicher, Sebastian Nuck, Lars Wesemann, Andreas Both, Martin Gaedke: From TMR to Turtle: Predicting Result Relevance from Mouse Cursor Interactions in Web Search; Pages 386-413; Journal of Web Engineering. Vol.14 No.5&6

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Research Area

Web Engineering

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