{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T08:28:53Z","timestamp":1777278533683,"version":"3.51.4"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T00:00:00Z","timestamp":1565568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2019,9,30]]},"abstract":"<jats:p>\n            Knowledge discovery from GPS trajectory data is an essential topic in several scientific areas, including data mining, human behavior analysis, and user modeling. This article proposes a task that assigns personalized visited points of interest (POIs). Its goal is to assign every fine-grain location (i.e., POIs) that a user actually visited, which we call\n            <jats:italic>visited-POI<\/jats:italic>\n            , to the corresponding span of his or her (personal) GPS trajectories. We also introduce a novel algorithm to solve this assignment task. First, we exhaustively extract stay-points as span candidates of visits using a variant of a conventional stay-point extraction method and then extract POIs that are located close to the extracted stay-points as visited-POI candidates. Then, we simultaneously predict which stay-points and POIs can be actual user visits by considering various aspects, which we formulate as integer linear programming. Experimental results conducted on a real user dataset show that our method achieves higher accuracy in the visited-POI assignment task than the various cascaded procedures of conventional methods.\n          <\/jats:p>","DOI":"10.1145\/3317667","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T14:41:50Z","timestamp":1565707310000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Personalized Visited-POI Assignment to Individual Raw GPS Trajectories"],"prefix":"10.1145","volume":"5","author":[{"given":"Jun","family":"Suzuki","sequence":"first","affiliation":[{"name":"NTT Communication Science Laboratories, Kyoto, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihiko","family":"Suhara","sequence":"additional","affiliation":[{"name":"NTT Service Evolution Laboratories, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroyuki","family":"Toda","sequence":"additional","affiliation":[{"name":"NTT Service Evolution Laboratories, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyosuke","family":"Nishida","sequence":"additional","affiliation":[{"name":"NTT Media Intelligence Laboratories, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,8,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1180639.1180857"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/862896.881068"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-003-0240-0"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2424321.2424348"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920968"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273513"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.400568"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020579"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872518.2890468"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD\u201996)","author":"Ester Martin","year":"1996","unstructured":"Martin Ester , Hans-Peter Kriegel , J\u00f6rg Sander , and Xiaowei Xu . 1996 . 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