{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:03:54Z","timestamp":1774631034444,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T00:00:00Z","timestamp":1528675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61462022"],"award-info":[{"award-number":["61462022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Technology Support Program","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}]},{"name":"National Key Technology Support Program","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}]},{"DOI":"10.13039\/501100013072","name":"Major Science and Technology Project of Hainan province","doi-asserted-by":"publisher","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}],"id":[{"id":"10.13039\/501100013072","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hainan Province","doi-asserted-by":"publisher","award":["617062"],"award-info":[{"award-number":["617062"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["kyqd1610"],"award-info":[{"award-number":["kyqd1610"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users\/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item\u2019s tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems.<\/jats:p>","DOI":"10.3390\/info9060143","type":"journal-article","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T11:01:01Z","timestamp":1528714861000},"page":"143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6932-3209","authenticated-orcid":false,"given":"Huirui","family":"Han","sequence":"first","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China"},{"name":"College of Information Science &amp; Technology, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxing","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China"},{"name":"College of Information Science &amp; Technology, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China"},{"name":"College of Information Science &amp; Technology, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-2783","authenticated-orcid":false,"given":"Uzair Aslam","family":"Bhatti","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China"},{"name":"College of Information Science &amp; Technology, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.eswa.2018.01.015","article-title":"Improving Memory-Based User Collaborative Filtering with Evolutionary Multi-Objective Optimization","volume":"98","author":"Karabadji","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_2","first-page":"36","article-title":"An Empirical Study of the Recursive Input Generation Algorithm for Memory-based Collaborative Filtering Recommender Systems","volume":"5","author":"Morozov","year":"2017","journal-title":"Int. J. Inf. Decis. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3062179","article-title":"Mitigating Data Sparsity Using Similarity Reinforcement-Enhanced Collaborative Filtering","volume":"17","author":"Hu","year":"2017","journal-title":"ACM Trans. Int. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s10479-016-2367-1","article-title":"Neighbor selection for user-based collaborative filtering using covering-based rough sets","volume":"256","author":"Zhang","year":"2017","journal-title":"Ann. Oper. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.chb.2014.12.011","article-title":"A collaborative user-centered framework for recommending items in Online Social Networks","volume":"51","author":"Colace","year":"2015","journal-title":"Comput. Hum. Behav."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.knosys.2012.07.021","article-title":"Towards a user based recommendation strategy for digital ecosystems","volume":"37","author":"Moscato","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.knosys.2017.01.027","article-title":"Factored similarity models with social trust for top-N item recommendation","volume":"122","author":"Guo","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1016\/j.im.2016.04.003","article-title":"SocoTraveler: Travel-package recommendations leveraging social influence of different relationship types","volume":"53","author":"He","year":"2016","journal-title":"Inf. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s11042-010-0480-8","article-title":"A multimedia recommender integrating object features and user behavior","volume":"50","author":"Albanese","year":"2010","journal-title":"Multimed. Tools Appl."},{"key":"ref_10","first-page":"11","article-title":"Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation","volume":"35","author":"Yin","year":"2016","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Manotumruksa, J., Macdonald, C., and Ounis, I. (2016, January 24\u201328). Regularising Factorised Models for Venue Recommendation using Friends and their Comments. Proceedings of the International Conference on Information and Knowledge Management, Indianapolis, IN, USA.","DOI":"10.1145\/2983323.2983889"},{"key":"ref_12","unstructured":"Kharrat, F.B., Elkhleifi, A., and Faiz, R. (December, January 29). Recommendation system based contextual analysis of Facebook comment. Proceedings of the International Conference of Computer Systems and Applications, Agadir, Morocco."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10489-016-0841-8","article-title":"Attributes coupling based matrix factorization for item recommendation","volume":"46","author":"Yu","year":"2017","journal-title":"Appl. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2414425.2414436","article-title":"Improving recommendation accuracy based on item-specific tag preferences","volume":"4","author":"Gedikli","year":"2013","journal-title":"ACM Trans. Int. Syst. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/TLT.2013.23","article-title":"Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments","volume":"6","author":"Chatti","year":"2013","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Peng, J., and Zeng, D. (2009, January 8\u201311). Topic-based web page recommendation using tags. Proceedings of the IEEE International Conference on Intelligence and Security Informatics, Dallas, TX, USA.","DOI":"10.1109\/ISI.2009.5137324"},{"key":"ref_17","first-page":"26","article-title":"Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging","volume":"3","author":"Blaze","year":"2014","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tso-Sutter, K.H.L., Marinho, L.B., and Schmidt-Thieme, L. (2008, January 16\u201320). Tag-aware recommender systems by fusion of collaborative filtering algorithms. Proceedings of the ACM Symposium on Applied Computing, Fortaleza, Brazil.","DOI":"10.1145\/1363686.1364171"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhen, Y., Li, W.J., and Yeung, D.Y. (2009, January 22\u201325). TagiCoFi: Tag informed collaborative filtering. Proceedings of the ACM Conference on Recommender Systems, New York, NY, USA.","DOI":"10.1145\/1639714.1639727"},{"key":"ref_20","first-page":"748","article-title":"Collaborative and content-based recommender system for social bookmarking website","volume":"68","author":"Huang","year":"2010","journal-title":"World Acad. Sci. Eng. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.elerap.2009.08.004","article-title":"Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation","volume":"9","author":"Kim","year":"2010","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1002\/sam.11184","article-title":"Content-boosted matrix factorization techniques for recommender systems","volume":"6","author":"Nguyen","year":"2013","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.knosys.2013.11.001","article-title":"Utilizing user tag-based interests in recommender systems for social resource sharing websites","volume":"56","author":"Huang","year":"2014","journal-title":"Knowl.-Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s10844-012-0227-2","article-title":"Folksonomy link prediction based on a tripartite graph for tag recommendation","volume":"40","author":"Rawashdeh","year":"2013","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, H.N., Rawashdeh, M., and Saddik, A.E. (2013, January 19\u201322). Tailoring recommendations to groups of users. Proceedings of the International Conference on Intelligent User Interfaces, Santa Monica, CA, USA.","DOI":"10.1145\/2449396.2449401"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.knosys.2015.03.008","article-title":"Addressing cold-start: Scalable recommendation with tags and keywords","volume":"83","author":"Ji","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fang, Z., Gao, S., Li, B., Li, J., and Liao, J. (2015, January 14\u201317). Cross-Domain Recommendation via Tag Matrix Transfer. Proceedings of the IEEE International Conference on Data Mining Workshop, Atlantic City, NJ, USA.","DOI":"10.1109\/ICDMW.2015.133"},{"key":"ref_28","unstructured":"Paterek, A. (2007, January 12). Improving regularized singular value decomposition for collaborative filtering. Proceedings of the Kdd Cup & Workshop, San Jose, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.knosys.2011.09.006","article-title":"Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization","volume":"27","author":"Luo","year":"2012","journal-title":"Knowl.-Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6160","DOI":"10.1109\/TSP.2016.2602809","article-title":"Learning Laplacian Matrix in Smooth Graph Signal Representations","volume":"64","author":"Dong","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1137\/070704277","article-title":"Robust Stochastic Approximation Approach to Stochastic Programming","volume":"19","author":"Nemirovski","year":"2014","journal-title":"SIAM J. Optim."},{"key":"ref_32","unstructured":"GroupLens (2018, June 10). MovieLens 20M Dataset. Available online: http:\/\/grouplens.org\/datasets\/movielens\/20m\/."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ziegler, C.N., McNee, S.M., Konstan, J.A., and Lausen, G. (2005, January 10\u201314). Improving recommendation lists through topic diversification. Proceedings of the International Conference on World Wide Web, Chiba, Japan.","DOI":"10.1145\/1060745.1060754"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix Factorization Techniques for Recommender Systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/6\/143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:08:13Z","timestamp":1760195293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/6\/143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,11]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["info9060143"],"URL":"https:\/\/doi.org\/10.3390\/info9060143","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,11]]}}}