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 → <city> beach’ has been observed, we are able to offer “Montezuma beach” as a recommendation, even if the two queries wer...

• Type: Journal
• Year: 2011
• Where: WWW
• Authors: Idan Szpektor, Aristides Gionis, Yoelle Maarek
• Keywords: Internet Technology, Long-tail Queries, Query Assistance, Query Recommendation, WWW 2011