{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:04:13Z","timestamp":1761807853367,"version":"3.41.0"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2013,11,1]],"date-time":"2013-11-01T00:00:00Z","timestamp":1383264000000},"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. Knowl. Discov. Data"],"published-print":{"date-parts":[[2013,11]]},"abstract":"<jats:p>Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.<\/jats:p>","DOI":"10.1145\/2541268.2541270","type":"journal-article","created":{"date-parts":[[2014,1,6]],"date-time":"2014-01-06T20:42:39Z","timestamp":1389040959000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Social trust prediction using heterogeneous networks"],"prefix":"10.1145","volume":"7","author":[{"given":"Jin","family":"Huang","sequence":"first","affiliation":[{"name":"University of Texas at Arlington, Arlington, TX"}]},{"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington, Arlington, TX"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington, Arlington, TX"}]},{"given":"Yi-Cheng","family":"Tu","sequence":"additional","affiliation":[{"name":"University of South Florida"}]},{"given":"Yu","family":"Lei","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington"}]}],"member":"320","published-online":{"date-parts":[[2013,12,25]]},"reference":[{"volume-title":"Proceedings of the 20th International Joint Conference on Artificial Intelligence. Morgan Kaufmann","author":"Bedi P.","key":"e_1_2_1_1_1","unstructured":"Bedi , P. , Kaur , H. , and Marwaha , S . 2007. Trust based recommender system for semantic web . In Proceedings of the 20th International Joint Conference on Artificial Intelligence. Morgan Kaufmann , San Francisco, CA, 2677--2682. Bedi, P., Kaur, H., and Marwaha, S. 2007. Trust based recommender system for semantic web. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, 2677--2682."},{"volume-title":"Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann","author":"Billsus D.","key":"e_1_2_1_2_1","unstructured":"Billsus , D. and Pazzani , M. J . 1998. Learning collaborative information filters . In Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann , San Francisco, CA, 46--54. Billsus, D. and Pazzani, M. J. 1998. Learning collaborative information filters. In Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, 46--54."},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Boyd S. and Vandenberghe L. 2004. Convex Optimization. Cambridge University Press Cambridge.   Boyd S. and Vandenberghe L. 2004. Convex Optimization. Cambridge University Press Cambridge.","DOI":"10.1017\/CBO9780511804441"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.57"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2184319.2184343"},{"volume-title":"Proceedings of the 27th International Conference on Machine Learning. ACM","author":"Cao B.","key":"e_1_2_1_6_1","unstructured":"Cao , B. , Liu , N. , and Yang , Q . 2010. Transfer learning for collective link prediction in multiple heterogenous domains . In Proceedings of the 27th International Conference on Machine Learning. ACM , New York, 159--166. Cao, B., Liu, N., and Yang, Q. 2010. Transfer learning for collective link prediction in multiple heterogenous domains. In Proceedings of the 27th International Conference on Machine Learning. ACM, New York, 159--166."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Chapelle O. Scholkopf B. and Zien A. 2006. Semi-Supervised Learning. MIT Press Cambridge MA.   Chapelle O. Scholkopf B. and Zien A. 2006. Semi-Supervised Learning. MIT Press Cambridge MA.","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401914"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.277"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1117454.1117456"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/988672.988727"},{"key":"e_1_2_1_13_1","unstructured":"He X. and Niyogi P. 2003. Locality preserving projections. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 153--160.  He X. and Niyogi P. 2003. Locality preserving projections. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 153--160."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/312624.312682"},{"volume-title":"Proceedings of the 16th International Joint Conference on Artificial Intelligence. ACM","author":"Hofmann T.","key":"e_1_2_1_15_1","unstructured":"Hofmann , T. and Puzicha , J . 1999. Latent class models for collaborative filtering . In Proceedings of the 16th International Joint Conference on Artificial Intelligence. ACM , New York, 688--693. Hofmann, T. and Puzicha, J. 1999. Latent class models for collaborative filtering. In Proceedings of the 16th International Joint Conference on Artificial Intelligence. ACM, New York, 688--693."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2398515"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775126"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/775152.775242"},{"key":"e_1_2_1_19_1","unstructured":"Lee D. and Seung H. 2000. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 556--562.  Lee D. and Seung H. 2000. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 556--562."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772756"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/956863.956972"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1297231.1297235"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Massa P. and Avesani P. 2009. Trust metrics in recommender systems. Comput. Social Trust 259--285.  Massa P. and Avesani P. 2009. Trust metrics in recommender systems. Comput. Social Trust 259--285.","DOI":"10.1007\/978-1-84800-356-9_10"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718519"},{"key":"e_1_2_1_26_1","unstructured":"Ng A. Jordan M. and Weiss Y. 2001. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 849--856.  Ng A. Jordan M. and Weiss Y. 2001. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 849--856."},{"volume-title":"Proceedings of the 24th AAAI Conference on Artificial Intelligence. AAAI Press","author":"Pan W.","key":"e_1_2_1_27_1","unstructured":"Pan , W. , Xiang , W. , Liu , N. , and Yang , Q . 2010. Transfer learning in collaborative filtering for sparsity reduction . In Proceedings of the 24th AAAI Conference on Artificial Intelligence. AAAI Press , Palo Alto, CA, 230--235. Pan, W., Xiang, W., Liu, N., and Yang, Q. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the 24th AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, CA, 230--235."},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Roweis S. and Saul L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 2323--2326.  Roweis S. and Saul L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 2323--2326.","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_2_1_29_1","unstructured":"Salakhutdinov R. and Mnih A. 2007. Probabilitistic matrix factorization. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 1257--1264.  Salakhutdinov R. and Mnih A. 2007. Probabilitistic matrix factorization. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 1257--1264."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390267"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401969"},{"volume-title":"Proceedings of the 20th Annual International Conference on Machine Learning. AAAI Press","author":"Srebro N.","key":"e_1_2_1_34_1","unstructured":"Srebro , N. and Jaakkola , T . 2003. Weighted low-rank approximations . In Proceedings of the 20th Annual International Conference on Machine Learning. AAAI Press , Palo Alto, CA, 720--727. Srebro, N. and Jaakkola, T. 2003. Weighted low-rank approximations. In Proceedings of the 20th Annual International Conference on Machine Learning. AAAI Press, Palo Alto, CA, 720--727."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835853"},{"volume-title":"Proceedings of the 21st International Joint Conference on Artificial Intelligence. Morgan Kaufmann","author":"Xu Z.","key":"e_1_2_1_37_1","unstructured":"Xu , Z. , Kersting , K. , and Tresp , V . 2009. Multi-relational learning with gaussian process . In Proceedings of the 21st International Joint Conference on Artificial Intelligence. Morgan Kaufmann , San Francisco, CA, 1309--1314. Xu, Z., Kersting, K., and Tresp, V. 2009. Multi-relational learning with gaussian process. In Proceedings of the 21st International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, 1309--1314."},{"key":"e_1_2_1_38_1","unstructured":"Yu K. and Chu W. 2007. Gaussian process models for link analysis and transfer learning. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 1657--1664.  Yu K. and Chu W. 2007. Gaussian process models for link analysis and transfer learning. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 1657--1664."},{"key":"e_1_2_1_39_1","unstructured":"Yu K. Chu W. Yu S. Tresp V. and Zhao X. 2006. Stochastic relational models for discriminative link prediction. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 333--340.  Yu K. Chu W. Yu S. Tresp V. and Zhao X. 2006. Stochastic relational models for discriminative link prediction. In Advances in Neural Information Processing Systems. MIT Press Cambridge MA 333--340."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553525"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/1077464.1077466"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277825"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2541268.2541270","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2541268.2541270","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:35:01Z","timestamp":1750232101000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2541268.2541270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,11]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2013,11]]}},"alternative-id":["10.1145\/2541268.2541270"],"URL":"https:\/\/doi.org\/10.1145\/2541268.2541270","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2013,11]]},"assertion":[{"value":"2012-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-12-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}