Organizing query completions for web search

All state-of-the-art web search engines implement an auto-completion mechanism - an assistive technology enabling users to effectively formulate their search queries by predicting the next characters or words that they are likely to type. Query completions (or suggestions) are typically mined from past user interactions with the search engine, e.g., from query logs, clickthrough patterns, or query reformulations; they are ranked by some measure of query popularity, e.g., query frequency or clickthrough rate. Current query suggestion tools largely assume that the set of suggestions provided to the users is homogeneous, corresponding to a single real-world interpretation of the query. In this paper, we hypothesize that, in some cases, users would benefit from an alternative presentation of the suggestions, one where suggestions are not only ordered by likelihood but also organized by high-level user intent. Rich search suggestion interaction frameworks that reduce the user effort in identifying the set of relevant suggestions open new and promising directions towards improving user experience. Along these lines, we propose clustering the set of suggestions presented to a search engine user, and assigning an appropriate label to each subset of suggestions to help users quickly identify useful ones. For this, we present a variety of unsupervised clustering techniques for search suggestions, based on the information available to a large-scale web search engine. We evaluate our novel search suggestion presentation techniques on a real-world dataset of query logs. Based on a set of user studies, we show that by extending the existing assistance layer to effectively group suggestions and label them - while accounting for the query popularity - we substantially increase the user's satisfaction.