  Location-based services (LBS) have become more and more ubiquitous recently.
Existing methods focus on finding relevant points-of-interest (POIs) based on
users' locations and query keywords. Nowadays, modern LBS applications generate
a new kind of spatio-textual data, regions-of-interest (ROIs), containing
region-based spatial information and textual description, e.g., mobile user
profiles with active regions and interest tags. To satisfy search requirements
on ROIs, we study a new research problem, called spatio-textual similarity
search: Given a set of ROIs and a query ROI, we find the similar ROIs by
considering spatial overlap and textual similarity. Spatio-textual similarity
search has many important applications, e.g., social marketing in
location-aware social networks. It calls for an efficient search method to
support large scales of spatio-textual data in LBS systems. To this end, we
introduce a filter-and-verification framework to compute the answers. In the
filter step, we generate signatures for the ROIs and the query, and utilize the
signatures to generate candidates whose signatures are similar to that of the
query. In the verification step, we verify the candidates and identify the
final answers. To achieve high performance, we generate effective high-quality
signatures, and devise efficient filtering algorithms as well as pruning
techniques. Experimental results on real and synthetic datasets show that our
method achieves high performance.
