document: Improving recommendation for long-tail queries via templates The ability to aggregate huge volumes of queries over a large population of users allows search engines to build precise models for a variety of query-assistance features such as query recommendation , correction , etc. . Yet , no matter how much data is aggregated , the long-tail distribution implies that a large fraction of queries are rare . As a result , most query assistance services perform poorly or are not even triggered on long-tail queries . We propose a method to extend the reach of query assistance techniques ( and in particular query recommendation ) to long-tail queries by reasoning about rules between query templates rather than individual query transitions , as currently done in query-flow graph models . As a simple example , if we recognize that ` Montezuma ' is a city in the rare query `` Montezuma surf '' and if the rule ` city surf → beach has been observed , we are able to offer `` Montezuma beach '' as a recommendation , even if the two queries were never observed in a same session . We conducted experiments to validate our hypothesis , first via traditional small-scale editorial assessments but more interestingly via a novel automated large scale evaluation methodology . Our experiments show that general coverage can be relatively increased by 24 % using templates without penalizing quality . Furthermore , for 36 % of the 95M queries in our query flow graph , which have no out edges and thus could not be served recommendations , we can now offer at least one recommendation in 98 % of the cases .
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