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.