Jigs and Lures: Associating Web Queries with Strongly-Typed Entities

We propose methods for estimating the probability
that an entity from an entity database
is associated with a web search query. Association
is modeled using a query entity click
graph, blending general query click logs with
vertical query click logs. Smoothing techniques
are proposed to address the inherent
data sparsity in such graphs, including interpolation
using a query synonymy model. A
large-scale empirical analysis of the smoothing
techniques, over a 2-year click graph
collected from a commercial search engine,
shows significant reductions in modeling error.
The association models are then applied
to the task of recommending products to web
queries, by annotating queries with products
from a large catalog and then mining queryproduct
associations through web search session
analysis. Experimental analysis shows
that our smoothing techniques improve coverage
while keeping precision stable, and overall,
that our top-performing model affects 9%
of general web queries with 94% precision.