Abstraction of query auto completion logs for anonymity-preserving analysis

Query auto completion (QAC) is used in search interfaces to interactively offer a list of suggestions to users as they enter queries. The suggested completions are updated each time the user modifies their partial query, as they either add further keystrokes or interact directly with completions that have been offered. In this work we use a state model to capture the possible interactions that can occur in a QAC environment. Using this model, we show how an abstract QAC log can be derived from a sequence of QAC interactions; this log does not contain the actual characters entered, but records only the sequence of types of interaction, thus preserving user anonymity with extremely high confidence. To validate the usefulness of the approach, we use a large scale abstract QAC log collected from a popular commercial search engine to demonstrate how previous and new knowledge about QAC behavior can be inferred without knowledge of the queries being entered. An interaction model is then derived from this log to demonstrate its validity, and we report observations on user behavior with QAC systems based on the interaction model that is proposed.