{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T23:06:17Z","timestamp":1772838377108,"version":"3.50.1"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,7,9]],"date-time":"2018-07-09T00:00:00Z","timestamp":1531094400000},"content-version":"vor","delay-in-days":189,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61532006"],"award-info":[{"award-number":["61532006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61320106006"],"award-info":[{"award-number":["61320106006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>Current recommender systems often take fusion factors into consideration to realize personalize point\u2010of\u2010interest (POI) recommendation. Historical behavior records and location factors are two kinds of significant features in most of recommendation scenarios. However, existing approaches usually use the Euclidean distance directly without considering the traffic factors. Moreover, the timing characteristics of users\u2019 historical behaviors are not fully utilized. In this paper, we took the restaurant recommendation as an example and proposed a personalized POI recommender system integrating the user profile, restaurant characteristics, users\u2019 historical behavior features, and subway network features. Specifically, the subway network features such as the number of passing stations, waiting time, and transfer times are extracted and a recurrent neural network model is employed to model user behaviors. Experiments were conducted on a real\u2010world dataset and results show that the proposed method significantly outperforms the baselines on two metrics.<\/jats:p>","DOI":"10.1155\/2018\/3698198","type":"journal-article","created":{"date-parts":[[2018,7,9]],"date-time":"2018-07-09T23:31:44Z","timestamp":1531179104000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Personalized POI Recommendation Based on Subway Network Features and Users\u2019 Historical Behaviors"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6553-3444","authenticated-orcid":false,"given":"Danfeng","family":"Yan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6490-7096","authenticated-orcid":false,"given":"Xuan","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8499-596X","authenticated-orcid":false,"given":"Zhengkai","family":"Guo","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2018,7,9]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"NatarajanS.andMohM. Recommending news based on hybrid user profile popularity trends and location Proceedings of the 2016 International Conference on Collaboration Technologies and Systems CTS 2016 November 2016 usa 204\u2013211 2-s2.0-85017028314.","DOI":"10.1109\/CTS.2016.0050"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21042-1_22"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/11827405_42"},{"key":"e_1_2_9_4_2","first-page":"1257","article-title":"Probabilistic matrix factorization","author":"Mnih A.","year":"2008","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_5_2","first-page":"556","article-title":"Algorithms for non-negative matrix factorization","author":"Lee D.","year":"2001","journal-title":"Advances in neural information processing systems"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2003.1167344"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"WangJ. De VriesA. P. andReindersM. J. T. Unifying user-based and item-based collaborative filtering approaches by similarity fusion Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval August 2006 ACM 501\u2013508 2-s2.0-33750345680.","DOI":"10.1145\/1148170.1148257"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2014.2355842"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"TewariA. S. KumarA. andBarmanA. G. Book recommendation system based on combine features of content based filtering collaborative filtering and association rule mining Proceedings of the 2014 4th IEEE International Advance Computing Conference IACC 2014 February 2014 ind 500\u2013503 2-s2.0-84899102057.","DOI":"10.1109\/IAdCC.2014.6779375"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2805701"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsc.2014.2320262"},{"key":"e_1_2_9_13_2","article-title":"Multi-dimensional QoS prediction for service recommendations","author":"Wang S.","year":"2016","journal-title":"IEEE Transaction on Services Computing"},{"key":"e_1_2_9_14_2","article-title":"QoS prediction for service recommendations in mobile edge computing","author":"Wang S.","year":"2017","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"DavidsonJ. LiebaldB. LiuJ. NandyP. andVan VleetT. The YouTube video recommendation system Proceedings of the 4th ACM Recommender Systems Conference (RecSys \u203210) September 2010 Barcelona Spain 293\u2013296 https:\/\/doi.org\/10.1145\/1864708.1864770 2-s2.0-78649975969.","DOI":"10.1145\/1864708.1864770"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"ZhaoX. LuanH. CaiJ.et al. Personalized video recommendation based on viewing history with the study on YouTube Proceedings of the International Conference on Internet Multimedia Computing and Service 2012 ACM 161\u2013165.","DOI":"10.1145\/2382336.2382382"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"SekoS. MotegiM. YagiT. andMutoS. Video content recommendation for group based on viewing history and viewer preference Proceedings of the 2011 IEEE International Conference on Consumer Electronics ICCE 2011 January 2011 usa 351\u2013352 2-s2.0-79952935655.","DOI":"10.1109\/ICCE.2011.5722622"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1509\/jmkr.45.1.77"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"CovingtonP. AdamsJ. andSarginE. Deep neural networks for youtube recommendations Proceedings of the 10th ACM Conference on Recommender Systems RecSys 2016 September 2016 191\u2013198 2-s2.0-84991244861.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"ZhengL. NorooziV. andYuP. S. Joint Deep Modeling of Users and Items Using Reviews for Recommendation Proceedings of the the Tenth ACM International Conference Feburary 2017 Cambridge United Kingdom 425\u2013434 https:\/\/doi.org\/10.1145\/3018661.3018665.","DOI":"10.1145\/3018661.3018665"},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"LiuK. ShiX. andNatarajanP. Sequential Heterogeneous Attribute Embedding for Item Recommendation Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW) November 2017 New Orleans LA 773\u2013780 https:\/\/doi.org\/10.1109\/ICDMW.2017.107.","DOI":"10.1109\/ICDMW.2017.107"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","unstructured":"DaiH. WangY. TrivediR. andSongL. Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation Proceedings of the the 1st Workshop September 2016 Boston MA USA 29\u201334 https:\/\/doi.org\/10.1145\/2988450.2988451.","DOI":"10.1145\/2988450.2988451"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"OkuraS. TagamiY. OnoS. andTajimaA. Embedding-based news recommendation for millions of users Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2017 August 2017 1933\u20131942 2-s2.0-85029044749.","DOI":"10.1145\/3097983.3098108"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"RendleS. Factorization machines Proceedings of the 10th IEEE International Conference on Data Mining ICDM 2010 December 2010 Australia 995\u20131000 2-s2.0-79951760087 https:\/\/doi.org\/10.1109\/ICDM.2010.127.","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"JuanY. ZhuangY. ChinW.-S. andLinC.-J. Field-aware factorization machines for CTR prediction Proceedings of the 10th ACM Conference on Recommender Systems RecSys 2016 September 2016 usa 43\u201350 2-s2.0-84991272078 https:\/\/doi.org\/10.1145\/2959100.2959134.","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/361002.361007"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/367766.368168"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"KorenY. Factorization meets the neighborhood: a multifaceted collaborative filtering model Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u203208) August 2008 New York NY USA 426\u2013434 https:\/\/doi.org\/10.1145\/1401890.1401944 2-s2.0-65449121157.","DOI":"10.1145\/1401890.1401944"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2018\/3698198.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2018\/3698198.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2018\/3698198","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T06:46:35Z","timestamp":1723013195000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2018\/3698198"}},"subtitle":[],"editor":[{"given":"Kok-Seng","family":"Wong","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,1]]}},"alternative-id":["10.1155\/2018\/3698198"],"URL":"https:\/\/doi.org\/10.1155\/2018\/3698198","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"value":"1530-8669","type":"print"},{"value":"1530-8677","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1]]},"assertion":[{"value":"2018-04-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-06-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-07-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3698198"}}