att_abstract={{Computational advertising has received a tremendous amount of attention from the business and academic community recently. While great advances have been made on modeling click-through rate in well studied settings like sponsored search and context match, local search, has received relatively less attention. The geographic nature of local search and associated local browsing makes interesting research challenges and opportunities possible. We consider a novel application of a relational regression model to local search. The model is attractive in that it allows us to explicitly control and represent geographic and category-based neighborhood style constraints on the samples that result in superior click-through rate estimates. Further, the relational regression model we fit allows us to estimate an interpretable inherent `quality' of a business listing which we demonstrate reveals interesting latent information about listings and is also useful for further analysis.
	att_authors={sb799t, sc984q, im3247},
	att_categories={C_CCF.5, C_CCF.9},
	att_copyright={{MIT Press, Neural Information Processing Systems (NIPS)}},
	att_copyright_notice={{The definitive version was published in NIPS 2010 Workshop: Machine Learning in Online ADvertising. {{, 2010-12-10}}{{, http://nips.cc/}}}},
	att_tags={computational advertising,  local search,  atti,  ctr modeling},
	author={Suhrid Balakrishnan and Sumit Chopra and Dan Melamed},
	institution={{NIPS 2010 Workshop: Machine Learning in Online ADvertising}},
	title={{The Business Next Door: Click-Through Rate Modeling for Local Search}},