TellMyRelevance! Predicting the Relevance of Web Search Results from Cursor Interactions
Proceedings of 22nd ACM International Conference on Information and Knowledge Management
It is crucial for the success of a search-driven web application to answer users' queries in the best possible way. A common approach is to use click models for guessing the relevance of search results. However, these models are imprecise and waive valuable information one can gain from non-click user interactions. We introduce TellMyRelevance!--a novel automatic end-to-end pipeline for tracking cursor interactions at the client, analyzing these and learning according relevance models. Yet, the models depend on the layout of the search results page involved, which makes them difficult to evaluate and compare. Thus, we use a Random Mouse Cursor as an extension to our pipeline for generating layout-dependent baselines. Based on these, we can perform evaluations of real-world relevance models. A large-scale user study showed that we can learn reasonably good relevance models that compare favorably to an existing state-of-the-art click model.
Speicher, Maximilian; Both, Andreas; Gaedke, Martin: TellMyRelevance! Predicting the Relevance of Web Search Results from Cursor Interactions. Proceedings of 22nd ACM International Conference on Information and Knowledge Management, pp. 1281-1290, 2013.