{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:14:07Z","timestamp":1766067247753,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user\u2019s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.<\/jats:p>","DOI":"10.3390\/sym12111930","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T11:50:34Z","timestamp":1606132234000},"page":"1930","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions"],"prefix":"10.3390","volume":"12","author":[{"given":"Alpamis","family":"Kutlimuratov","sequence":"first","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do 461-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5923-8695","authenticated-orcid":false,"given":"Akmalbek","family":"Abdusalomov","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do 461-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taeg Keun","family":"Whangbo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Gachon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do 461-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowl. Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., Shapira, B., and Kantor, P.B. (2011). Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-0-387-85820-3"},{"key":"ref_3","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":"IEEE Comput."},{"key":"ref_4","first-page":"1","article-title":"Deep Learning based Recommender System: A Survey and New Perspectives","volume":"52","author":"Zhang","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"39745","DOI":"10.1109\/ACCESS.2018.2853107","article-title":"Recommendation to groups of users the singularities concept","volume":"6","author":"Ortega","year":"2018","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Tuzhilin","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Su, X., and Khoshgoftaar, T.M. (2009). A survey of collaborative filtering techniques. Adv. Artif. Intell.","DOI":"10.1155\/2009\/421425"},{"key":"ref_8","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":"2012","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/138859.138867","article-title":"Using collaborative filtering to weave an information tapestry","volume":"35","author":"Goldberg","year":"1992","journal-title":"Commun. ACM"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/TSC.2015.2433251","article-title":"Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation","volume":"9","author":"Liu","year":"2016","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Herlocker, J.L., Konstan, J.A., Borchers, A., and Riedl, J. (1999, January 15\u201319). An algorithmic framework for performing collaborative filtering. Proceedings of the SIGIR\u201999: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA.","DOI":"10.1145\/312624.312682"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11349","DOI":"10.1109\/ACCESS.2019.2891544","article-title":"Cold start recommendation based on attribute-fused singular value decomposition","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, J., Sun, Z., Bozzon, A., and Zhang, J. (2016, January 15\u201319). Learning hierarchical feature influence for recommendation by recursive regularization. Proceedings of the Recsys: 10th ACM Conference on Recommender System, Boston, MA, USA.","DOI":"10.1145\/2959100.2959159"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Koren, Y., and Bell, R. (2011). Advances in collaborative filtering. Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-0-387-85820-3_5"},{"key":"ref_15","unstructured":"(2006). Unifying User-Based and Item-Based Collaborative Filtering Approaches by Similarity Fusion, ACM. SIGIR \u201906."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zarei, M.R., and Moosavi, M.R. (2019, January 6\u20137). A Memory-Based Collaborative Filtering Recommender System Using Social Ties. Proceedings of the 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), Tehran, Iran.","DOI":"10.1109\/PRIA.2019.8786023"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Stephen, S.C., Xie, H., and Rai, S. (2017, January 17\u201319). Measures of similarity in memory-based collaborative filtering recommender system: A comparison. Proceedings of the 4th Multidisciplinary International Social Networks Conference, 4th Multidisciplinary International Social Networks Conference (MISNC), Bangkok, Thailand.","DOI":"10.1145\/3092090.3092105"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Al-bashiri, H., Abdulgabber, M.A., Romli, A., and Kahtan, H. (2018). An improved memory-based collaborative filtering method based on the TOPSIS technique. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0204434"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, X., and Li, D. (2019). An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy. Mobile Inform. Syst.","DOI":"10.1155\/2019\/3560968"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fang, Y., and Si, L. (2011). Matrix co-factorization for recommendation with rich side information and implicit feedback. Hetrec 11, ACM.","DOI":"10.1145\/2039320.2039330"},{"key":"ref_21","unstructured":"Kumar, A., and Sodera, N. (2017, January 5\u20136). Open problems in recommender systems diversity. Proceedings of the International Conference on Computing, Communication and Automation (ICCCA2017), Greater Noida, India."},{"key":"ref_22","unstructured":"Salakhutdinov, R., and Mnih, A. (2007, January 3\u20136). Probabilistic matrix factorization. Proceedings of the NIPS\u201907: 20th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Seo, S., Huang, J., Yang, H., and Liu, Y. (2017). Interpretable convolutional neural networks with dual local and global attention for review rating prediction. Recsys \u201917, ACM.","DOI":"10.1145\/3109859.3109890"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1016\/j.knosys.2013.02.016","article-title":"A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles","volume":"47","author":"Maleszka","year":"2013","journal-title":"Knowl. Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ge, M., Elahi, M., Tobias, I.F., Ricci, F., and Massimo, D. (2015, January 18\u201320). Using tags and latent factors in a food recommender system. Proceedings of the DH \u201915: 5th International Conference on Digital Health, Florence, Italy.","DOI":"10.1145\/2750511.2750528"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Garg, N., and Weber, I. (2008, January 23\u201325). Personalized, interactive tag recommendation for flickr. Proceedings of the 2nd ACM International Conference on Recommender Systems, RecSys\u201908, Lausanne, Switzerland.","DOI":"10.1145\/1454008.1454020"},{"key":"ref_27","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 collaborative filtering algorithms. Proceedings of the SAC \u201908: 2008 ACM Symposium on Applied Computing, Fortaleza, Brazil.","DOI":"10.1145\/1363686.1364171"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Schein, A.I., Popescul, A., Ungar, L.H., and Pennock, D.M. (2002, January 11\u201315). Methods and metrics for cold-start recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland.","DOI":"10.1145\/564376.564421"},{"key":"ref_29","unstructured":"Vall, A., Skowron, M., and Schedl, M. (2015). Improving Music Recommendations with a Weighted Factorization of the Tagging Activity, ISMIR."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1007\/s10115-016-0925-0","article-title":"Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation","volume":"49","author":"Shi","year":"2016","journal-title":"Knowl. Inform. Syst."},{"key":"ref_31","unstructured":"Wu, J., Chen, L., Yu, Q., Han, P., and Wu, Z. (2015). Trust-Aware Media Recommendation in Heterogeneous Social Networks, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lu, K., Zhang, G., Li, R., Zhang, S., and Wang, B. (2012). Exploiting and exploring hierarchical structure in music recommendation. AIRS 2012: Information Retrieval Technology, Springer.","DOI":"10.1007\/978-3-642-35341-3_18"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.neucom.2014.09.082","article-title":"Hierarchical Itemspace Rank: Exploiting hierarchy to alleviate sparsity in ranking-based recommendation","volume":"163","author":"Nikolakopoulos","year":"2015","journal-title":"J. Neurocomput."},{"key":"ref_34","unstructured":"Wang, Z., Wang, Y., and Wu, H. (2010, January 7). Tag meet ratings: Improving collaborative filtering with tag-based neighborhood method. Proceedings of the SRS\u201910 ACM, Hong Kong, China."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shepitsen, A., Gemmell, J., Mobasher, M., and Burke, R. (2008, January 23\u201325). Personalized recommendation in social tagging systems using hierarchical clustering. Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys, Lausanne, Switzerland.","DOI":"10.1145\/1454008.1454048"},{"key":"ref_36","unstructured":"Chung, F. (1997). Spectral Graph Theory, American Mathematical Society."},{"key":"ref_37","unstructured":"Trigeorgis, G., Bousmalis, K., Zaferiou, S., and Schuller, B. (2014, January 21\u201326). A deep semi-nmf model for learning hidden representations. Proceedings of the 31st International Conference on Machine Learning (ICML-14), Beijing, China."},{"key":"ref_38","first-page":"556","article-title":"Algorithms for non-negative matrix factorization","volume":"13","author":"Lee","year":"2001","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ding, C., Li, T., Peng, W., and Park, H. (2006, January 20\u201323). Orthogonal nonnegative matrix t-factorizations for clustering. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA.","DOI":"10.1145\/1150402.1150420"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gu, Q., Zhou, J., and Ding, C.H.Q. (May, January 29). Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs. Proceedings of the 2010 SIAM International Conference on Data Mining, Columbus, OH, USA.","DOI":"10.1137\/1.9781611972801.18"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lam, X.N., Vu, T., Le, T.D., and Duong, A.D. (2008, January 31). Addressing cold-start problem in recommendation systems. Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, Suwon, Korea.","DOI":"10.1145\/1352793.1352837"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/11\/1930\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:36:19Z","timestamp":1760178979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/11\/1930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":41,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["sym12111930"],"URL":"https:\/\/doi.org\/10.3390\/sym12111930","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,11,23]]}}}