Term-by-Term Query Auto-Completion for Mobile Search

With the ever increasing usage of mobile search, where text input is typically slow and error-prone, assisting users to formulate their queries contributes to a more satisfactory search experience. Query auto-completion (QAC) techniques, which predict possible completions for user queries, are the archetypal example of query assistance and are present in most search engines. We argue, however, that classic QAC, which operates by suggesting whole-query completions, may be sub-optimal for the case of mobile search as the available screen real estate to show suggestions is limited and editing is typically slower than in desktop search. In this paper we propose the idea of term-by-term QAC, which is a new technique inspired by predictive keyboards that suggests to the user one term at a time, instead of whole-query completions. We describe an efficient mechanism to implement this technique and an adaptation of a prior user model to evaluate the effectiveness of both standard and term-by-term QAC approaches using query log data. Our experiments with a mobile query log from a commercial search engine show the validity of our approach according to this user model with respect to saved characters, saved terms and examination effort. Finally, a user study provides further insights about our term-by-term technique compared with standard QAC with respect to the variables analyzed in the query log-based evaluation and additional variables related to the successfulness, the speed of the interactions and the properties of the submitted queries.