{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:13:39Z","timestamp":1761808419595,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,17]],"date-time":"2021-01-17T00:00:00Z","timestamp":1610841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of China","award":["61801277","61373081"],"award-info":[{"award-number":["61801277","61373081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people\u2019s behaviors. Misleading information refers to false information made up by some malicious marketer to create panic and seek benefits. In particular, when emergency events break out, many users may be misled by the misleading information on the Internet, which further leads them to buy things that are not in line with their actual needs. We call this kind of human activity \u2018emergency consumption\u2019, which not only fails to reflect users\u2019 true interests but also causes the phenomenon of user preference deviation, and thus lowers the accuracy of the personal recommender system. Although traditional recommendation models have proven useful in capturing users\u2019 general interests from user\u2013item interaction records, learning to predict user interest accurately is still a challenging problem due to the uncertainty inherent in user behavior and the limited information provided by user\u2013item interaction records. In addition, to deal with the misleading information, we divide user information into two types, namely explicit preference information (explicit comments or ratings) and user side information (which can show users\u2019 real interests and will not be easily affected by misleading information), and then we create a deep social recommendation model which fuses user side information called FSCR. The FSCR model is significantly better than existing baseline models in terms of rating prediction and system robustness, especially in the face of misleading information; it can effectively identify the misleading users and complete the task of rating prediction well.<\/jats:p>","DOI":"10.3390\/info12010037","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T05:17:34Z","timestamp":1610947054000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FSCR: A Deep Social Recommendation Model for Misleading Information"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9612-5497","authenticated-orcid":false,"given":"Depeng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7321-4646","authenticated-orcid":false,"given":"Hongchen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4854-6331","authenticated-orcid":false,"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moscato, V., Picariello, A., and Sperli, G. (2020). An emotional recommender system for music. IEEE Intell. Syst.","DOI":"10.1109\/MIS.2020.3026000"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.future.2018.04.006","article-title":"Multimedia story creation on social networks","volume":"86","author":"Amato","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, L., Jiang, H., Wang, X., and Liu, F. (2017). Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs. Information, 8.","DOI":"10.3390\/info8010020"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yin, H., Chen, T., Hung, Q.V.N., Huang, Z., and Cui, L. (2020). GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection. SIGIR, 689\u2013698.","DOI":"10.1145\/3397271.3401165"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Vilakone, P., and Park, D.-S. (2020). The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System. Appl. Sci., 10.","DOI":"10.3390\/app10082939"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., and Kashef, R. (2020). Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Appl. Sci., 10.","DOI":"10.3390\/app10217748"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liao, X., Li, X., Xu, Q., Wu, H., and Wang, Y. (2020). Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction. Appl. Sci., 10.","DOI":"10.3390\/app10207245"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_9","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":"Adomavicius","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1023\/A:1006544522159","article-title":"A Framework for Collaborative, Content-Based and Demographic Filtering","volume":"13","author":"Pazzani","year":"1999","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1016\/j.ins.2007.02.036","article-title":"Using SVD and demographic data for the enhancement of generalized Collaborative Filtering","volume":"177","author":"Vozalis","year":"2007","journal-title":"Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, R., Wu, X., Wu, X., and Wang, W. (2020, January 20\u201324). Few-Shot Learning for New User Recommendation in Location-based Social Networks. Proceedings of the WWW \u201920: The Web Conference 2020, Taipei, Taiwan.","DOI":"10.1145\/3366423.3379994"},{"key":"ref_13","unstructured":"Liu, Y., Chen, L., He, X., Peng, J., Zheng, Z., and Tang, J. (2020). Modelling high-order social relations for item recommendation. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3305260","article-title":"Combating Fake News: A Survey on Identification and Mitigation Techniques","volume":"10","author":"Sharma","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_15","unstructured":"Budak, C., Agrawal, D., and El Abbadi, A. (April, January 28). Limiting the spread of misinformation in social networks. Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1257\/jep.31.2.211","article-title":"Social Media and Fake News in the 2016 Election","volume":"31","author":"Allcott","year":"2017","journal-title":"J. Econ. Perspect."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Takayasu, M., Sato, K., Sano, Y., Yamada, K., Miura, W., and Takayasu, H. (2015). Rumor diffusion and convergence during the 3.11 earthquake: A Twitter case study. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0121443"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gupta, A., Kumaraguru, P., Castillo, C., and Meier, P. (2014). Tweetcred: Real-time credibility assessment of content on twitter. International Conference on Social Informatics, Springer.","DOI":"10.1007\/978-3-319-13734-6_16"},{"key":"ref_19","first-page":"22","article-title":"COVID-19 on Facebook Ads: Competing Agendas around a Public Health Crisis","volume":"Volume 285","author":"Mejova","year":"2020","journal-title":"Proceedings of the COMPASS \u201920: ACM SIGCAS Conference on Computing and Sustainable Societies"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qian, F., Gong, C., Sharma, K., and Liu, Y. (2018, January 13\u201319). Neural user response generator: Fake news detection with collective user intelligence. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/533"},{"key":"ref_21","unstructured":"Ott, M., Cardie, C., and Hancock, J.T. (2013, January 9\u201314). Negative deceptive opinion spam. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), Westin Peachtree Plaza Hotel, Atlanta, GA, USA."},{"key":"ref_22","unstructured":"Ott, M., Choi, Y., Cardie, C., and Hancock, J.T. (2011, January 19\u201324). Finding deceptive opinion spam by any stretch of the imagination. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Portland, OR, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gu, Y., Shibukawa, T., Kondo, Y., Nagao, S., and Kamijo, S. (2020). Prediction of Stock Performance Using Deep Neural Networks. Appl. Sci., 10.","DOI":"10.3390\/app10228142"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhang, Z., Yan, J., Zhang, N., Zha, H., Li, G., Li, Y., and Yu, Q. (2020). A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting. Appl. Sci., 10.","DOI":"10.3390\/app10238491"},{"key":"ref_25","unstructured":"Wang, W.Y. (August, January 30). \u201cLiar, Liar Pants on Fire\u201d: A new benchmark dataset for fake news detection. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, X., He, Z., Du, X., and Chua, T.S. (2018, January 8\u201312). Adversarial Personalized Ranking for Recommendation. Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA.","DOI":"10.1145\/3209978.3209981"},{"key":"ref_27","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2009, January 18\u201321). BPR: Bayesian Personalized Ranking from Implicit Feedback. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Deldjoo, Y., Di Noia, T., and Merra, F.A. (2020). A survey on Adversarial Recommender Systems: From Attack\/Defense strategies to Generative Adversarial Networks. arXiv.","DOI":"10.1145\/3439729"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/TKDE.2019.2893638","article-title":"Adversarial Training Towards Robust Multimedia Recommender System","volume":"32","author":"Tang","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Anelli, V.W., Deldjoo, Y., Di Noia, T., and Merra, F.A. (2020, January 26). Adversarial Learning for Recommendation: Applications for Security and Generative Tasks-Concept to Code. Proceedings of the 14th ACM Conference on Recommender Systems (RecSys\u201920), Rio de Janeiro, Brazil.","DOI":"10.1145\/3383313.3411447"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Di Noia, T., Malitesta, D., and Merra, F.A. (July, January 29). TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems. Proceedings of the 2020 50th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), Valencia, Spain.","DOI":"10.1109\/DSN-W50199.2020.00011"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, R., Wu, X., and Wang, W. (2020, January 3\u20137). Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks. Proceedings of the WSDM \u201920: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston TX, USA.","DOI":"10.1145\/3336191.3371841"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, F., Yin, R., Zhang, K., Trajcevski, G., Zhong, T., and Wu, J. (2019). Adversarial Point-of-Interest Recommendation. World Wide Web Conference, ACM.","DOI":"10.1145\/3308558.3313609"},{"key":"ref_34","unstructured":"Kumar, S., and Gupta, M.D. (2019). C+GAN: Complementary Fashion Item Recommendation. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, X., Zhang, X., Cao, Y., and Xia, M. (2019, January 10\u201316). VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19, Macao, China.","DOI":"10.24963\/ijcai.2019\/584"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1644873.1644874","article-title":"Factor in the neighbors: Scalable and accurate collaborative filtering","volume":"4","author":"Koren","year":"2010","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_37","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. J."},{"key":"ref_38","unstructured":"Salakhutdinov, R., and Mnih, A. (2007, January 3\u20134). Probabilistic matrix factorization. Proceedings of the NIPS\u201907: Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS 2007, Vancouver, BC, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fan, W., Ma, Y., and Li, Q. (2019). Graph neural networks for social recommendation. The World Wide Web Conference 2019, ACM.","DOI":"10.1145\/3308558.3313488"},{"key":"ref_40","unstructured":"Hao, M., and Yang, H. (2008, January 26\u201330). Sorec: Social recommendation using probabilistic matrix factorization. Proceedings of the CIKM \u201908: Proceedings of the 17th ACM Conference on Information and Knowledge Management, Napa Valley, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lu, Y., Yan, W., and Shah, C. (2017, January 27\u201329). Collaborative user network embedding for social recommender systems. Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, TX, USA.","DOI":"10.1137\/1.9781611974973.43"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/1\/37\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:12:08Z","timestamp":1760159528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/1\/37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,17]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["info12010037"],"URL":"https:\/\/doi.org\/10.3390\/info12010037","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2021,1,17]]}}}