Novel Query Suggestions
Ilona Nawrot, Oskar Gross, Antoine Doucet, Hannu T. T. Toivonen · 2014
Query auto-completion (QAC) is one of the most recognizable and widely used services of modern search engines. Its goal is to assist a user in the process of query formulation. Current QAC systems are mainly reactive. They respond to the present request using past knowledge. Specifically, they mostly rely on query logs analysis or corpus terms co-occurrences and rank suggestions according to their similarity with the partial user query, their past popularity, or their temporal dynamics features (e.g. trends, bursts, seasonality in query popularity). Consequently, a suggestion to be recommended by the QAC system must be preceded with a substantial users' interest and ipso facto must be an old information. However, a growing amount of people turns to search engines to find novel information, that is emergent or recently created (not redundant) one. Conventional QAC systems are thus unable to fulfill the increasingly real-time needs of the users.