Selectively Personalizing Query Auto-Completion

Abstract

Query auto-completion (QAC) is being used by many of today's search engines. It helps searchers formulate queries by providing a list of query completions after entering an initial prefix of a query. To cater for a user's specific information needs, personalized QAC strategies use a searcher's search history and their profile. Is personalization consistently effective in different search contexts? We study the QAC problem by selectively personalizing the query completion list. Based on a lenient personalized QAC strategy that encodes the ranking signal as a trade-off between query popularity and search context, we propose a model for selectively personalizing query auto-completion (SP-QAC) to study this trade-off. We predict effective trade-offs based on a regression model, where the typed query prefix, clicked documents and preceding queries in the same session are used to weigh personalization in QAC. Experiments on the AOL query log show the SP-QAC model can significantly outperform a state-of-the-art personalized QAC approach.