{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T20:56:52Z","timestamp":1762376212094,"version":"3.41.2"},"reference-count":39,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T00:00:00Z","timestamp":1489968000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"National Natural Science Foundation of China Beijing Municipal Commission of Education","award":["61502047"],"award-info":[{"award-number":["61502047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2017,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The rapid growth of event\u2010based social networks (EBSNs) has generated great demand for personalized event recommendation. Indeed, new events are published every day in EBSNs. In this paper, we focus on the problem of recommending new events (i.e., the newly published events that have not yet received any user response) to registered users in EBSNs. Notice that collaborative filtering is ineffective for recommending new events due to the short lifetime of events and the lack of historical information, that is, the cold\u2010start problem. A straightforward approach to address this problem is to adopt content\u2010based recommendation techniques, for example, deriving user interests through the content information of the events they attended. Nevertheless, we observe that organizer influence and geographical preference also play important roles in a user's decision to participate in an event. Motivated by this observation, we combine user interest, organizer influence, and geographical preference to present a unified model for new event recommendation. In particular, we utilize a topic model to derive user interests while adopting matrix factorization to infer organizer influences on users from the interactions between users and organizers. Moreover, we model users' geographical preferences via their location histories of attended events. We conduct a performance evaluation using real\u2010world data collected from DoubanEvent. The experimental results demonstrate the effectiveness of our proposed recommendation model.<\/jats:p>","DOI":"10.1111\/exsy.12190","type":"journal-article","created":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T09:44:07Z","timestamp":1490003047000},"source":"Crossref","is-referenced-by-count":7,"title":["Exploiting organizer influence and geographical preference for new event recommendation"],"prefix":"10.1111","volume":"34","author":[{"given":"Shuchen","family":"Li","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications  Beijing China"}]},{"given":"Xiang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications  Beijing China"}]},{"given":"Sen","family":"Su","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications  Beijing China"}]},{"given":"Haonan","family":"Sun","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University  Pittsburgh PA USA"}]}],"member":"311","published-online":{"date-parts":[[2017,3,20]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1162\/jmlr.2003.3.4-5.993"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"Cheng C. Yang H. King I. &Lyu M. R.(2012).Fused matrix factorization with geographical and social influence in location\u2010based social networks. InAAAI.Toronto Ontario Canada pp.17\u201323.","DOI":"10.1609\/aaai.v26i1.8100"},{"key":"e_1_2_8_4_1","unstructured":"Cheng H. Ye J. &Zhu Z.(2013).What\u0160s your next move: User activity prediction in location\u2010based social networks. InProceedings of the 13th SIAM International Conference on Data Mining Austin Texas USA pp.171\u2013179."},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"deMacedo A. Q. Marinho L. B. &Santos R. L. T.(2015).Context\u2010aware event recommendation in event\u2010based social networks. InProceedings of the 9th ACM Conference on Recommender Systems RecSys 2015 Vienna Austria pp.123\u2013130.","DOI":"10.1145\/2792838.2800187"},{"key":"e_1_2_8_6_1","doi-asserted-by":"crossref","unstructured":"Du R. Yu Z. Mei T. Wang Z. Wang Z. &Guo B.(2014).Predicting activity attendance in event\u2010based social networks: Content context and social influence. InUbiComp Seattle WA USA pp.425\u2013434.","DOI":"10.1145\/2632048.2632063"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2012.2188804"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2011.2162946"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2012.2188899"},{"key":"e_1_2_8_10_1","unstructured":"Heinrich G.(2005).Parameter estimation for text analysis.University of Leipzig Tech. Rep. Technical Report Version 2.9."},{"key":"e_1_2_8_11_1","doi-asserted-by":"crossref","unstructured":"Hofmann T.(1999).Probabilistic latent semantic indexing. InSIGIR Berkeley CA USA pp.50\u201357.","DOI":"10.1145\/312624.312649"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963774"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/23270012.2016.1198242"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511763113"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_2_8_16_1","unstructured":"Lee D. D. &Seung H. S.(2000).Algorithms for non\u2010negative matrix factorization. InNIPS Denver CO USA pp.556\u2013562."},{"key":"e_1_2_8_17_1","doi-asserted-by":"crossref","unstructured":"Liao G. Zhao Y. Xie S. &Yu P. S.(2013).An effective latent networks fusion based model for event recommendation in offline ephemeral social networks. InCIKM San Francisco CA USA pp.1655\u20131660.","DOI":"10.1145\/2505515.2505605"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"Liu B. Fu Y. Yao Z. &Xiong H.(2013).Learning geographical preferences for point\u2010of\u2010interest recommendation. InKDD Chicago IL USA pp.1043\u20131051.","DOI":"10.1145\/2487575.2487673"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"Liu B. &Xiong H.(2013).Point\u2010of\u2010interest recommendation in location based social networks with topic and location awareness. InSIAM Austin Texas USA pp.396\u2013404.","DOI":"10.1137\/1.9781611972832.44"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"Liu X. He Q. Tian Y. Lee W.\u2010C. McPherson J. &Han J.(2012).Event\u2010based social networks: Linking the online and offline social worlds. InKDD Beijing China pp.1032\u20131040.","DOI":"10.1145\/2339530.2339693"},{"key":"e_1_2_8_21_1","doi-asserted-by":"crossref","unstructured":"Minkov E. Charrow B. Ledlie J. Teller S. J. &Jaakkola T.(2010).Collaborative future event recommendation. InCIKM Toronto Ontario Canada pp.819\u2013828.","DOI":"10.1145\/1871437.1871542"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10799-015-0228-2"},{"key":"e_1_2_8_23_1","doi-asserted-by":"crossref","unstructured":"Pham T. N. Li X. Cong G. &Zhang Z.(2015).A general graph\u2010based model for recommendation in event\u2010based social networks. In31st IEEE International Conference on Data Engineering ICDE 2015 Seoul South Korea pp.567\u2013578.","DOI":"10.1109\/ICDE.2015.7113315"},{"key":"e_1_2_8_24_1","doi-asserted-by":"crossref","unstructured":"Qiao Z. Zhang P. Cao Y. Zhou C. Guo L. &Fang B.(2014).Combining heterogenous social and geographical information for event recommendation. InAAAI Qu\u00e9bec City Qu\u00e9bec Canada pp.145\u2013151.","DOI":"10.1609\/aaai.v28i1.8725"},{"key":"e_1_2_8_25_1","doi-asserted-by":"crossref","unstructured":"Qiao Z. Zhang P. Zhou C. Cao Y. Guo L. &Zhang Y.(2014).Event recommendation in event\u2010based social networks. InAAAI Qu\u00e9bec City Qu\u00e9bec Canada pp.3130\u20133131.","DOI":"10.1609\/aaai.v28i1.9095"},{"key":"e_1_2_8_26_1","unstructured":"Resnik P. &Hardisty E.(2010).Gibbs Sampling for the uninitiated.DTIC Document."},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12031"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3"},{"key":"e_1_2_8_29_1","unstructured":"Salakhutdinov R. &Mnih A.(2007).Probabilistic matrix factorization. InNIPS Vancouver British Columbia Canada 1257\u20131264."},{"key":"e_1_2_8_30_1","doi-asserted-by":"crossref","unstructured":"Salakhutdinov R. &Mnih A.(2008).Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. InICML Helsinki Finland pp.880\u2013887.","DOI":"10.1145\/1390156.1390267"},{"key":"e_1_2_8_31_1","doi-asserted-by":"crossref","unstructured":"Schmidt M. N. Winther O. &Hansen L. K.(2009).Bayesian non\u2010negative matrix factorization. InICA Paraty Brazil pp.540\u2013547.","DOI":"10.1007\/978-3-642-00599-2_68"},{"key":"e_1_2_8_32_1","doi-asserted-by":"crossref","unstructured":"Wang C. &Blei D. M.(2011).Collaborative topic modeling for recommending scientific articles. InKDD San Diego CA USA pp.448\u2013456.","DOI":"10.1145\/2020408.2020480"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2016.07.002"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2300753"},{"key":"e_1_2_8_35_1","doi-asserted-by":"crossref","unstructured":"Ye M. Yin P. &Lee W.(2010).Location recommendation for location\u2010based social networks. InSIGSPATIAL San Jose CA USA pp.458\u2013461.","DOI":"10.1145\/1869790.1869861"},{"key":"e_1_2_8_36_1","doi-asserted-by":"crossref","unstructured":"Ye M. Yin P. Lee W.\u2010C. &Lee D. L.(2011).Exploiting geographical influence for collaborative point\u2010of\u2010interest recommendation. InSIGIR Beijing China pp.325\u2013334.","DOI":"10.1145\/2009916.2009962"},{"key":"e_1_2_8_37_1","doi-asserted-by":"crossref","unstructured":"Yin H. Sun Y. Cui B. Hu Z. &Chen L.(2013).LCARS: A location\u2010content\u2010aware recommender system. InKDD Chicago IL USA pp.221\u2013229.","DOI":"10.1145\/2487575.2487608"},{"issue":"1","key":"e_1_2_8_38_1","first-page":"2:1","article-title":"Mining geographic\u2010temporal\u2010semantic patterns in trajectories for location prediction","volume":"5","author":"Ying J. J.","year":"2013","journal-title":"ACM TIST, New York, NY, USA"},{"key":"e_1_2_8_39_1","doi-asserted-by":"crossref","unstructured":"Yuan Q. Cong G. Ma Z. Sun A. &Magnenat\u2010Thalmann N.(2013).Time\u2010aware point\u2010of\u2010interest recommendation. InSIGIR Dublin Ireland pp.363\u2013372.","DOI":"10.1145\/2484028.2484030"},{"key":"e_1_2_8_40_1","doi-asserted-by":"crossref","unstructured":"Zhang W. Wang J. &Feng W.(2013).Combining latent factor model with location features for event\u2010based group recommendation. InKDD Chicago IL USA pp.910\u2013918.","DOI":"10.1145\/2487575.2487646"}],"container-title":["Expert Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2Fexsy.12190","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.12190","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T22:45:55Z","timestamp":1694472355000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/exsy.12190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,20]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,4]]}},"alternative-id":["10.1111\/exsy.12190"],"URL":"https:\/\/doi.org\/10.1111\/exsy.12190","archive":["Portico"],"relation":{},"ISSN":["0266-4720","1468-0394"],"issn-type":[{"type":"print","value":"0266-4720"},{"type":"electronic","value":"1468-0394"}],"subject":[],"published":{"date-parts":[[2017,3,20]]},"article-number":"e12190"}}