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Autocompletion services help users in formulating queries by exploiting past queries. In this paper we propose methods for improving such services; specifically methods for increasing the number and the quality of the suggested "completions". In particular, we propose a novel method for partitioning the internal data structure that keeps the suggestions, making autocompletion services more scalable and faster. In addition we introduce a ranking method which promotes a suggestion that can lead to many other suggestions. The experimental and empirical results are promising.

Advancing Search Query Autocompletion Services with More and Better Suggestions