{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:34:49Z","timestamp":1750221289056,"version":"3.41.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,5,27]],"date-time":"2018-05-27T00:00:00Z","timestamp":1527379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Spanish Government","award":["PR2015-00231 and TIN2015-70648-P"],"award-info":[{"award-number":["PR2015-00231 and TIN2015-70648-P"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2018,5,31]]},"abstract":"<jats:p>The use of queries to find products and services that are located nearby is increasing rapidly due mainly to the ubiquity of internet access and location services provided by smartphone devices. Local search engines help users by matching queries with a predefined geographical connotation (\u201clocal queries\u201d) against a database of local business listings.<\/jats:p>\n          <jats:p>Local search differs from traditional Web search because, to correctly capture users\u2019 click behavior, the estimation of relevance between query and candidate results must be integrated with geographical signals, such as distance. The intuition is that users prefer businesses that are physically closer to them or in a convenient area (e.g., close to their home). However, this notion of closeness depends upon other factors, like the business category, the quality of the service provided, the density of businesses in the area of interest, the hour of the day, or even the day of the week.<\/jats:p>\n          <jats:p>\n            In this work, we perform an extensive analysis of online users\u2019 interactions with a local search engine, investigating their intent, temporal patterns, and highlighting relationships between distance-to-business and other factors, such as business reputation, Furthermore, we investigate the problem of estimating the click-through rate on local search (\n            <jats:italic>LCTR<\/jats:italic>\n            ) by exploiting the combination of standard retrieval methods with a rich collection of geo-, user-, and business-dependent features. We validate our approach on a large log collected from a real-world local search service. Our evaluation shows that the non-linear combination of business and user information, geo-local and textual relevance features leads to a significant improvements over existing alternative approaches based on a combination of relevance, distance, and business reputation [1].\n          <\/jats:p>","DOI":"10.1145\/3157059","type":"journal-article","created":{"date-parts":[[2018,5,29]],"date-time":"2018-05-29T12:24:26Z","timestamp":1527596666000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Characterizing and Predicting Users\u2019 Behavior on Local Search Queries"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6438-1661","authenticated-orcid":false,"given":"Fidel","family":"Cacheda","sequence":"first","affiliation":[{"name":"University of A Coru\u00f1a, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roi","family":"Blanco","sequence":"additional","affiliation":[{"name":"University of A Coru\u00f1a, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola","family":"Barbieri","sequence":"additional","affiliation":[{"name":"Tumblr, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,5,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526713"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"volume-title":"Proceedings of the Machine Learning Online Advertisement Workshop (MLOAD'10)","author":"Balakrishnan Suhrid","key":"e_1_2_1_3_1","unstructured":"Suhrid Balakrishnan , Sumit Chopra , and I. Dan Melamed . 2010. The business next door: Click-through rate modeling for local search . In Proceedings of the Machine Learning Online Advertisement Workshop (MLOAD'10) . 14--19. Suhrid Balakrishnan, Sumit Chopra, and I. Dan Melamed. 2010. The business next door: Click-through rate modeling for local search. In Proceedings of the Machine Learning Online Advertisement Workshop (MLOAD'10). 14--19."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2010021"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063983"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018683"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526711"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1502650.1502686"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/57167.57214"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(01)00065-2"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/11853565_20"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v014.i15"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1316948.1316950"},{"volume-title":"The Social Logic of Space","author":"Hillier Bill","key":"e_1_2_1_15_1","unstructured":"Bill Hillier and Julienne Hanson . 1984. The Social Logic of Space . Cambridge University Press . Bill Hillier and Julienne Hanson. 1984. The Social Logic of Space. 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