{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:12:52Z","timestamp":1769829172062,"version":"3.49.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,3,4]]},"abstract":"<jats:p>The major challenge of recommendation system (RS) based on implict feedback is to accurately model users\u2019 preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users\u2019 preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users\u2019 non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users\u2019 ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators.<\/jats:p>","DOI":"10.3233\/jifs-210123","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:08:39Z","timestamp":1638886119000},"page":"2915-2923","source":"Crossref","is-referenced-by-count":4,"title":["NCGAN:A neural adversarial collaborative filtering for recommender system"],"prefix":"10.1177","volume":"42","author":[{"given":"Jinyang","family":"Sun","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Ningbo University in Ningbo, Zhejiang, China"}]},{"given":"Baisong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ningbo University in Ningbo, Zhejiang, China"}]},{"given":"Hao","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ningbo University in Ningbo, Zhejiang, China"}]},{"given":"Weiming","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ningbo University in Ningbo, Zhejiang, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-210123_ref1","first-page":"2019","article-title":"Deep Learning Based Recommender System: A Survey and New Perspectives[J]","volume":"52","author":"Zhang","journal-title":"ACM Computing Surveys"},{"issue":"8","key":"10.3233\/JIFS-210123_ref2","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix Factorization Techniques for Recommender Systems[J]","volume":"42","author":"Koren","year":"2009","journal-title":"IEEE Computer"},{"issue":"5","key":"10.3233\/JIFS-210123_ref3","first-page":"2381","article-title":"MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender 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Informatics"},{"issue":"05","key":"10.3233\/JIFS-210123_ref7","doi-asserted-by":"crossref","first-page":"9322","DOI":"10.1609\/aaai.v34i05.6472","article-title":"Attentive User-Engaged Adversarial Neural Network for Community Question Answering[C]\/\/","volume":"34","author":"Xie","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-210123_ref8","first-page":"515","article-title":"IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models[C]","author":"Wang","year":"2017","journal-title":"International Acm Sigir Conference on Research and Development in Information Retrieval"},{"key":"10.3233\/JIFS-210123_ref9","unstructured":"Jang E. , Gu S. and Poole B. , Categorical reparameterization with gumbel-softmax[J], arXiv preprint arXiv:1611.01144, 2016."},{"key":"10.3233\/JIFS-210123_ref10","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1145\/3394486.3403253","article-title":"Dual Channel Hypergraph Collaborative Filtering[C]","author":"Ji","year":"2020","journal-title":"KDD"},{"issue":"04","key":"10.3233\/JIFS-210123_ref11","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1609\/aaai.v34i04.6184","article-title":"A knowledge-aware attentional reasoning network for recommendation[C]\/\/","volume":"34","author":"Zhu","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"04","key":"10.3233\/JIFS-210123_ref12","doi-asserted-by":"crossref","first-page":"6267","DOI":"10.1609\/aaai.v34i04.6094","article-title":"Multi-component graph convolutional collaborative filtering[C]\/\/","volume":"34","author":"Wang","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-210123_ref13","first-page":"1161","article-title":"Autoint: Automatic feature interaction learning via self-attentive neural networks[C]\/\/","author":"Song","year":"2019","journal-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management"},{"key":"10.3233\/JIFS-210123_ref14","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1145\/3336191.3371805","article-title":"LARA: Attribute-to-feature adversarial learning for new-item recommendation[C]\/\/","author":"Sun","year":"2020","journal-title":"Proceedings of the 13th International Conference on Web Search and Data Mining"},{"key":"10.3233\/JIFS-210123_ref15","first-page":"355","article-title":"Adversarial personalized ranking fo rrecommendation[C]\/\/","author":"He","year":"2018","journal-title":"The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval"},{"key":"10.3233\/JIFS-210123_ref16","first-page":"452","article-title":"BPR: Bayesian personalized ranking from implicit feedback[C]","author":"Rendle","year":"2009","journal-title":"Uncertainty in Artificial 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