Detecting and understanding implicit measures of user satisfaction
\nare essential for meaningful experimentation aimed at enhancing
\nweb search quality. While most existing studies on satisfaction
\nprediction rely on users\u2019 click activity and query reformulation
\nbehavior, o\u0089en such signals are not available for all search sessions
\nand as a result, not useful in predicting satisfaction. On the other
\nhand, user interaction data (such as mouse cursor movement) is
\nfar richer than just click data and can provide useful signals for
\npredicting user satisfaction. In this work, we focus on considering
\nholistic view of user interaction with the search engine result
\npage (SERP) and construct detailed universal interaction sequences
\nof their activity. We propose novel ways of leveraging the universal
\ninteraction sequences to automatically extract informative,
\ninterpretable subsequences. In addition to extracting frequent, discriminatory
\nand interleaved subsequences, we propose a Hawkes
\nprocess model to incorporate temporal aspects of user interaction.
\n\u008crough extensive experimentation we show that encoding the
\nextracted subsequences as features enables us to achieve signi\u0080-
\ncant improvements in predicting user satisfaction. We additionally
\npresent an analysis of the correlation between various subsequences
\nand user satisfaction. Finally, we demonstrate the usefulness of the
\nproposed approach in covering abandonment cases. Our \u0080ndings
\nprovide a valuable tool for \u0080ne-grained analysis of user interaction
\nbehavior for metric development.<\/p>\n","protected":false},"excerpt":{"rendered":"
Detecting and understanding implicit measures of user satisfaction are essential for meaningful experimentation aimed at enhancing web search quality. While most existing studies on satisfaction prediction rely on users\u2019 click activity and query reformulation behavior, o\u0089en such signals are not available for all search sessions and as a result, not useful in predicting satisfaction. 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