{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:21:55Z","timestamp":1777890115828,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2016,2,18]],"date-time":"2016-02-18T00:00:00Z","timestamp":1455753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Web Intelligence"],"published-print":{"date-parts":[[2016,2,18]]},"abstract":"<jats:p>Real-time recommendation of Twitter users based on the content of their profiles is a very challenging task. Traditional IR methods such as TF-IDF fail to handle efficiently large datasets. In this paper we present a scalable approach that allows real time recommendation of users based on their tweets. Our model builds a graph of terms, driven by the fact that users sharing similar interests will share similar terms. We show how this model can be encoded as a compact binary footprint, that allows very fast comparison and ranking, taking full advantage of modern CPU architectures. We validate our approach through an empirical evaluation against the Apache Lucene\u2019s implementation of TF-IDF. We show that our approach is in average two hundred times faster than standard optimized implementation of TF-IDF with a precision of 58%.<\/jats:p>","DOI":"10.3233\/web-160329","type":"journal-article","created":{"date-parts":[[2016,2,23]],"date-time":"2016-02-23T11:46:19Z","timestamp":1456227979000},"page":"17-29","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-time, scalable, content-based Twitter users recommendation"],"prefix":"10.1177","volume":"14","author":[{"given":"Julien","family":"Subercaze","sequence":"first","affiliation":[{"name":"Laboratoire Hubert Curien, UMR CNRS 5516, Universite Jean Monnet, 25 rue docteur Remy Annino, F-42000, Saint-Etienne, France. 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in: Proc. of the 17th International Conference on World Wide Web, ACM, 2008, pp.\u00a01041\u20131042.","DOI":"10.1145\/1367497.1367646"},{"issue":"3","key":"ref011","first-page":"135","volume":"3","author":"D\u00edaz A.","year":"2005","journal-title":"Web Intelligence and Agent Systems"},{"key":"ref012","unstructured":"[12]U.\u00a0Drepper, What every programmer should know about memory,\n                      Red Hat, Inc.\n                      , 11, 2007."},{"key":"ref013","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2003.813506"},{"key":"ref014","unstructured":"[14]A.\u00a0Go, L.\u00a0Huang and R.\u00a0Bhayani, Twitter sentiment analysis,\n                      Entropy\n                      2009\n                      (June 2009), 17."},{"key":"ref015","doi-asserted-by":"crossref","unstructured":"[15]J.\u00a0Hannon, M.\u00a0Bennett and B.\u00a0Smyth, Recommending Twitter users to follow using content and collaborative filtering approaches, in: Proc. of the 4th ACM Conference on Recommender Systems, ACM, 2010, pp.\u00a0199\u2013206.","DOI":"10.1145\/1864708.1864746"},{"key":"ref016","doi-asserted-by":"crossref","unstructured":"[16]J.\u00a0Hannon, M.\u00a0Bennett and B.\u00a0Smyth, Recommending Twitter users to follow using content and collaborative filtering approaches, in: Proc. of the 4th ACM Conference on Recommender Systems, RecSys \u201910, ACM, New York, NY, USA, 2010, pp.\u00a0199\u2013206.","DOI":"10.1145\/1864708.1864746"},{"key":"ref017","doi-asserted-by":"crossref","unstructured":"[17]O.\u00a0Hartig, C.\u00a0Bizer and J.C.\u00a0Freytag, Executing sparql queries over the web of linked data, in: The Semantic Web-ISWC 2009. 2009, pp.\u00a0293\u2013309.","DOI":"10.1007\/978-3-642-04930-9_19"},{"key":"ref018","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963772"},{"key":"ref019","doi-asserted-by":"crossref","unstructured":"[19]A.\u00a0Java, X.\u00a0Song, T.\u00a0Finin and B.\u00a0Tseng, Why we Twitter: Understanding microblogging usage and communities, in: Proc. of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, WebKDD\/SNA-KDD \u201907, ACM, NY, USA, 2007, pp.\u00a056\u201365.","DOI":"10.1145\/1348549.1348556"},{"key":"ref020","doi-asserted-by":"publisher","DOI":"10.1145\/245108.245123"},{"key":"ref021","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"ref022","unstructured":"[22]J.\u00a0Lafferty, A.\u00a0McCallum and F.\u00a0Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Morgan Kaufmann, San Francisco, CA, 2001, pp.\u00a0282\u2013289."},{"issue":"3","key":"ref023","first-page":"217","volume":"2","author":"Lingras P.","year":"2004","journal-title":"Web Intelligence and Agent Systems"},{"key":"ref024","doi-asserted-by":"crossref","unstructured":"[24]P.\u00a0Lops, M.\u00a0Gemmis and G.\u00a0Semeraro, Content-based recommender systems: State of the art and trends recommender systems handbook, in: Recommender Systems Handbook, F.\u00a0Ricci, L.\u00a0Rokach, B.\u00a0Shapira and P.B.\u00a0Kantor, eds, Springer US, Boston, MA, 2011, pp.\u00a073\u2013105, Chapter 3.","DOI":"10.1007\/978-0-387-85820-3_3"},{"key":"ref025","unstructured":"[25]C.\u00a0man Au Yeung, N.\u00a0Gibbins and N.\u00a0Shadbolt, A study of user profile generation from folksonomies, in: SWKM, P.\u00a0Dolog, M.\u00a0Kr\u00f6tzsch, S.\u00a0Schaffert and D.\u00a0Vrandecic, eds, CEUR Workshop Proceedings, Vol.\u00a0356, CEUR-WS.org, 2008."},{"key":"ref026","doi-asserted-by":"publisher","DOI":"10.1142\/S0218213004001466"},{"key":"ref027","unstructured":"[27]P.\u00a0Melville, R.J.\u00a0Mooney and R.\u00a0Nagarajan, Content-boosted collaborative filtering for improved recommendations, in: Eighteenth National Conference on Artificial Intelligence, AAAI, Menlo Park, CA, USA, 2002, pp.\u00a0187\u2013192."},{"key":"ref028","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2005.05.006"},{"key":"ref029","doi-asserted-by":"crossref","unstructured":"[29]S.\u00a0Mu\u00f1oz, J.\u00a0P\u00e9rez and C.\u00a0Gutierrez, Minimal deductive systems for rdf, in: The Semantic Web: Research and Applications, Springer, 2007, pp.\u00a053\u201367.","DOI":"10.1007\/978-3-540-72667-8_6"},{"key":"ref030","unstructured":"[30]A.\u00a0Pak and P.\u00a0Paroubek, Twitter as a corpus for sentiment analysis and opinion mining, in: Proc. of LREC 2010, 2010."},{"key":"ref031","doi-asserted-by":"publisher","DOI":"10.1007\/s13174-010-0003-x"},{"key":"ref032","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2005.09.002"},{"key":"ref033","doi-asserted-by":"crossref","unstructured":"[33]M.\u00a0Pennacchiotti and S.\u00a0Gurumurthy, Investigating topic models for social media user recommendation, in: Proc. of the 20th International Conference Companion on World Wide Web, ACM, 2011, pp.\u00a0101\u2013102.","DOI":"10.1145\/1963192.1963244"},{"key":"ref034","doi-asserted-by":"crossref","unstructured":"[34]O.\u00a0Phelan, K.\u00a0McCarthy and B.\u00a0Smyth, Using Twitter to recommend real-time topical news, in: Proc. of the Third ACM Conference on Recommender Systems, ACM, 2009, pp.\u00a0385\u2013388.","DOI":"10.1145\/1639714.1639794"},{"key":"ref035","doi-asserted-by":"publisher","DOI":"10.1145\/361219.361220"},{"key":"ref036","doi-asserted-by":"crossref","unstructured":"[36]J.B.\u00a0Schafer, D.\u00a0Frankowski, J.\u00a0Herlocker and S.\u00a0Sen, Collaborative filtering recommender systems, in: The Adaptive Web, Springer-Verlag, Berlin, Heidelberg, 2007, pp.\u00a0291\u2013324.","DOI":"10.1007\/978-3-540-72079-9_9"},{"key":"ref037","doi-asserted-by":"crossref","unstructured":"[37]Y.\u00a0Song, Z.\u00a0Zhuang, H.\u00a0Li, Q.\u00a0Zhao, J.\u00a0Li, W.C.\u00a0Lee and C.L.\u00a0Giles, Real-time automatic tag recommendation, in: Proc. of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2008, pp.\u00a0515\u2013522.","DOI":"10.1145\/1390334.1390423"},{"key":"ref038","doi-asserted-by":"crossref","unstructured":"[38]J.\u00a0Stan, V.H.\u00a0Do and P.\u00a0Maret, Semantic user interaction profiles for better people recommendation, in: ASONAM, 2011, pp.\u00a0434\u2013437.","DOI":"10.1109\/ASONAM.2011.21"},{"key":"ref039","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2005.06.001"},{"key":"ref040","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2934"},{"issue":"2","key":"ref041","first-page":"117","volume":"4","author":"Vozalis M.","year":"2006","journal-title":"Web Intelligence and Agent Systems"},{"key":"ref042","doi-asserted-by":"crossref","unstructured":"[42]H.M.\u00a0Wallach, Topic modeling: Beyond bag-of-words, in: Proc. of the 23rd International Conference on Machine Learning, ACM, 2006, pp.\u00a0977\u2013984.","DOI":"10.1145\/1143844.1143967"},{"key":"ref043","doi-asserted-by":"crossref","unstructured":"[43]C.\u00a0Wilson, B.\u00a0Boe, A.\u00a0Sala, K.P.N.\u00a0Puttaswamy and Y.B.\u00a0Zhao, User interactions in social networks and their implications, in: Proc. of the 4th ACM European Conference on Computer Systems, EuroSys \u201909, ACM, New York, NY, USA, 2009, pp.\u00a0205\u2013218.","DOI":"10.1145\/1519065.1519089"},{"key":"ref044","unstructured":"[44]W.\u00a0Wu, B.\u00a0Zhang and M.\u00a0Ostendorf, Automatic generation of personalized annotation tags for Twitter users, in: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, 2010, pp.\u00a0689\u2013692."},{"key":"ref045","doi-asserted-by":"crossref","unstructured":"[45]E.\u00a0Zangerle, W.\u00a0Gassler and G.\u00a0Specht, Using tag recommendations to homogenize folksonomies in microblogging environments, in: Social Informatics, 2011, pp.\u00a0113\u2013126.","DOI":"10.1007\/978-3-642-24704-0_16"},{"key":"ref046","unstructured":"[46]T.\u00a0Zesch and I.\u00a0Gurevych, Approximate matching for evaluating keyphrase extraction, in: Proc. of the 7th International Conference on Recent Advances in Natural Language Processing, Citeseer, 2009, pp.\u00a0484\u2013489."}],"container-title":["Web 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