Paper Info
Title
Predicting web search success with fine-grained interaction data
Abstract
Detecting and predicting searcher success is essential for automatically evaluating and improving Web search engine performance. In the past, Web searcher behavior data, such as result clickthrough, dwell time, and query reformulation sequences, have been successfully used for a variety of tasks, including prediction of success in a search session. However, the effectiveness of the previous approaches has been limited, as they tend to ignore how searchers actually view and interact with the visited pages. We show that fine-grained interactions, such as mouse cursor movements and scrolling, provide additional clues for better predicting success of a search session as a whole. To this end, we identify patterns of examination and interaction behavior that correspond to search success, and design a new Fine-grained Session Behavior (FSB) model to capture these patterns. Our experimental results show that FSB is significantly more effective than the state-of-the-art approaches that do not use these additional interaction data.
Year
DOI
Venue
2012
10.1145/2396761.2398570
CIKM
Keywords
Field
DocType
additional interaction data,search session,interaction behavior,web search engine performance,dwell time,web searcher behavior data,searcher success,predicting web search success,fine-grained interaction,additional clue,fine-grained interaction data
Dwell time,Web search engine,Data mining,Information retrieval,Computer science,Pointer (user interface),Scrolling
Conference
Citations 
PageRank 
References 
21
0.66
14
Authors
3
Name
Order
Citations
PageRank
Qi Guo171634.09
Dmitry Lagun235114.96
Eugene Agichtein34549269.70