{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:04Z","timestamp":1777703344602,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,5,12]],"date-time":"2019-05-12T00:00:00Z","timestamp":1557619200000},"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":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,7,9]]},"abstract":"<jats:p>For the acquisition of user behavior preference in social network, usually a data mining will be conducted on the nearest neighborhood users or latestprojects based on the user\u2019s historical behavior data, so as to find similar behaviorrelationship for quantitative analysis; it can also focus on the awareness on the user-related context information based on cognitive psychology, so as to find its internal links forthe potential mining. However, these methods ignore the intrinsic link between the browsing behavior and the preferred topics in the user link connection, resulting in the limited precision and accuracy of the preference acquisition. Inspired by the theory of complex network link prediction and the topic model, anacquisition method for users\u2019 browsing behavior preference was proposed in this paper. In the multi-dimensional network link environment, by measuring the importance of the node via network centrality and search the social network link via setting the similarity threshold, the real-time multi-link information and the big data about users\u2019 browsing under each link were acquired, then the data were filteredand cleaned by using adjustable parameters. On this basis, according to the least squares criterion the data underwent a fusion and were used to construct a data node distribution model for user browsing behavior, then the frequent feature items of user browsing behavior preference were extracted. Based on the extracted feature terms, the variational Bayes approximation reasoning method was used to construct the preference topic model. Finally, the hierarchical VSM model representation method was used to establish the preference acquisition model of user browsing behavior, and the model was updated in real time by user feedback processing mechanism. The experimental results on the real data set showed that the link search method and the preference topic model provided by this paper are accurate and efficient. Compared with the classical cooperative filtering method and the context-awareness method, the precision, accuracy and effectiveness of the preference acquisition model provided this paper are significantly improved, and its adaptability has been significantly strengthened.<\/jats:p>","DOI":"10.3233\/jifs-179103","type":"journal-article","created":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T11:34:36Z","timestamp":1558092876000},"page":"493-508","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Acquisition method of users\u2019 browsing behavior preference based on the fusion of social network link and theme model"],"prefix":"10.1177","volume":"37","author":[{"given":"Xin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Business, Central South University, Changsha Hunan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanju","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Business, Central South University, Changsha Hunan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongrun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Business, Central South University, Changsha Hunan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,5,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1057\/ejis.2014.3"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/0013916511404408"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2015.10.027"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/SW-2012-0080"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2327053"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9153"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.272"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.09.016"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-012-9135-y"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-014-2437-9"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/2414425.2414435"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.09.017"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13177-014-0097-9"},{"issue":"99","key":"e_1_3_2_15_2","first-page":"1","article-title":"User preference-based dual-memory neural model with memory consolidation approach","author":"Nasir J.","year":"2017","unstructured":"J.Nasir, Y.H.Yoo, D.H.Kim and J.H.Kim, User preference-based dual-memory neural model with memory consolidation approach, IEEE Transactions on Neural Networks & Learning SystemsPP(99) (2017), 1\u201315.","journal-title":"IEEE Transactions on Neural Networks & Learning Systems"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.socnet.2012.05.003"},{"issue":"11007","key":"e_1_3_2_17_2","article-title":"A high-reproducibility and high-accuracy method for automated topic classification","volume":"5","author":"Lancichinetti A.","year":"2016","unstructured":"A.Lancichinetti, M.I.Sirer, J.X.Wang, D.Acuna, K.Krding and L.A.N.Amaral, A high-reproducibility and high-accuracy method for automated topic classification, Physical Review X5(11007) (2016).","journal-title":"Physical Review X"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trit.2016.12.001"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510612113"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-015-0001-y"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-014-0334-3"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30139-6_24"},{"issue":"1","key":"e_1_3_2_23_2","first-page":"64","article-title":"Link prediction in social networks based on decision analysis","volume":"20","author":"Yong-Li L.I.","year":"2017","unstructured":"L.I.Yong-Li, L.Peng, and Z.Shu-Rui. Link prediction in social networks based on decision analysis. Journal of Management Sciences in China20(1) (2017), 64\u201374.","journal-title":"Journal of Management Sciences in China"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.05.036"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2444-z"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1137\/13094918X"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2011.2178325"},{"key":"e_1_3_2_28_2","article-title":"A social ties-based approach for group decision-making problems with incomplete additive preference relations","volume":"119","author":"Liang Q.","year":"2016","unstructured":"Q.Liang, X.Liao and J.Liu, A social ties-based approach for group decision-making problems with incomplete additive preference relations, Knowledge-Based Systems119 (2016).","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2013.05.059"},{"key":"e_1_3_2_30_2","first-page":"752","article-title":"EXTRACT: New extraction algorithm of association rules from frequent itemsets","author":"Feddaoui I.","year":"2016","unstructured":"I.Feddaoui, F.Felhi and J.Akaichi, EXTRACT: New extraction algorithm of association rules from frequent itemsets, Ieee\/acm International Conference on Advances in Social Networks Analysis and Mining IEEE, 2016, pp. 752\u2013756.","journal-title":"Ieee\/acm International Conference on Advances in Social Networks Analysis and Mining IEEE"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2016.09.002"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2015.12.015"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1002\/smj.2416"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2016.12.015"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2016.01.005"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-017-0708-5"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179103","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-179103","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:38:30Z","timestamp":1777455510000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-179103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,12]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,7,9]]}},"alternative-id":["10.3233\/JIFS-179103"],"URL":"https:\/\/doi.org\/10.3233\/jifs-179103","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,12]]}}}