{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T13:34:25Z","timestamp":1780666465121,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819723898","type":"print"},{"value":"9789819723904","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2390-4_30","type":"book-chapter","created":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T18:02:02Z","timestamp":1714240922000},"page":"439-453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generative Adversarial Networks Based on\u00a0Contrastive Learning for\u00a0Sequential Recommendation"],"prefix":"10.1007","author":[{"given":"Li","family":"Jianhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Taotao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun","family":"Chengyuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Dequan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,28]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Bharadhwaj, H., Park, H., Lim, B.: RecGAN: recurrent generative adversarial networks for recommendation systems. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 372\u2013376 (2018)","DOI":"10.1145\/3240323.3240383"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Chae, D., Kang, J., Kim, S., et al.: CFGAN: a generic collaborative filtering framework based on generative adversarial networks. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 137\u2013146 (2018)","DOI":"10.1145\/3269206.3271743"},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"37650","DOI":"10.1109\/ACCESS.2019.2905876","volume":"7","author":"D Chae","year":"2019","unstructured":"Chae, D., Shin, J., Kim, S.: Collaborative adversarial autoencoders: an effective collaborative filtering model under the GAN framework. IEEE Access 7, 37650\u201337663 (2019)","journal-title":"IEEE Access"},{"key":"30_CR4","unstructured":"Chen, X., Li, S., Li, H., et al.: Generative adversarial user model for reinforcement learning based recommendation system. In: Proceedings of the International Conference on Machine Learning, pp. 1052\u20131061 (2019)"},{"issue":"1","key":"30_CR5","doi-asserted-by":"publisher","first-page":"121","DOI":"10.3233\/IDA-216400","volume":"27","author":"J Li","year":"2023","unstructured":"Li, J., Li, J., Wang, C., et al.: Wide and deep generative adversarial networks for recommendation system. Intell. Data Anal. 27(1), 121\u2013136 (2023)","journal-title":"Intell. Data Anal."},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Flanagan, A., Oyomno, W., Grigorievskiy, A., et al.: Federated multi-view matrix factorization for personalized recommendations. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 324\u2013347 (2020)","DOI":"10.1007\/978-3-030-67661-2_20"},{"key":"30_CR7","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.neucom.2020.12.114","volume":"435","author":"Y Li","year":"2021","unstructured":"Li, Y., Wang, Q., Zhang, J.: The theoretical research of generative adversarial networks: an overview. Neurocomputing 435, 26\u201341 (2021)","journal-title":"Neurocomputing"},{"key":"30_CR8","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., et al.: Improved training of Wasserstein GANs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5769\u20135779 (2017)"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., et al.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Hu, B., Shi, C., Zhao, W., et al.: Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1531\u20131540 (2018)","DOI":"10.1145\/3219819.3219965"},{"key":"30_CR11","unstructured":"Huang, M., Li, H., Bai, B., et al.: A federated multi-view deep learning framework for privacy-preserving recommendations. arXiv preprint arXiv:2008.10808(2020)"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 659\u2013667 (2013)","DOI":"10.1145\/2487575.2487589"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Plaat, A., Kosters, W., Preuss, M.: High-accuracy model-based reinforcement learning, a survey. Artif. Intell. Rev. 56(9), 9541\u20139573 (2023). https:\/\/doi.org\/10.1007\/s10462-022-10335-w","DOI":"10.1007\/s10462-022-10335-w"},{"issue":"8","key":"30_CR14","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"issue":"7553","key":"30_CR15","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Lu, G., Zhao, Z., Gao, X., et al.: SRecGAN: pairwise adversarial training for sequential recommendation. In: Proceedings of the International Conference on Database Systems for Advanced Applications, pp. 20\u201335 (2021)","DOI":"10.1007\/978-3-030-73200-4_2"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Peng, S., Zeng, R., Liu, H., et al.: Emotion classification of text based on BERT and broad learning system. In: Proceedings of the APWeb-WAIM International Joint Conference on Web and Big Data, pp. 382\u2013396 (2021)","DOI":"10.1007\/978-3-030-85896-4_30"},{"issue":"5","key":"30_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3527848","volume":"55","author":"H Karimi","year":"2022","unstructured":"Karimi, H., Barthe, G., Scholkopf, B.: A survey of algorithmic recourse: contrastive explanations and consequential recommendations. ACM Comput. Surv. 55(5), 1\u201329 (2022)","journal-title":"ACM Comput. Surv."},{"issue":"7","key":"30_CR19","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1007\/s13042-021-01288-7","volume":"12","author":"F Qian","year":"2021","unstructured":"Qian, F., Huang, Y., Li, J., et al.: DLSA: dual-learning based on self-attention for rating prediction. Int. J. Mach. Learn. Cybern. 12(7), 1993\u20132005 (2021)","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"12","key":"30_CR20","doi-asserted-by":"publisher","first-page":"4482","DOI":"10.1007\/s10489-020-01648-w","volume":"50","author":"F Qian","year":"2020","unstructured":"Qian, F., Li, J., Du, X., et al.: Generative image inpainting for link prediction. Appl. Intell. 50(12), 4482\u20134494 (2020)","journal-title":"Appl. Intell."},{"key":"30_CR21","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., et al.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012)"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285\u2013295 (2001)","DOI":"10.1145\/371920.372071"},{"key":"30_CR23","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neucom.2021.02.046","volume":"441","author":"P Weerakody","year":"2021","unstructured":"Weerakody, P., Wong, K., Wang, G.: A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing 441, 161\u2013178 (2021)","journal-title":"Neurocomputing"},{"key":"30_CR24","unstructured":"Shi, J., Ji, H., Shi, C., Wang, X., Zhang, Z., Zhou, J.: Heterogeneous graph neural network for recommendation. arXiv preprint arXiv:2009.00799(2020)"},{"key":"30_CR25","unstructured":"Sun, X., Liu, H., Jing, L., et al.: Deep generative recommendation based on list-wise ranking. J. Comput. Res. Dev. 57(8), 1697\u20131706 (2020)"},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"Tong, Y., Luo, Y., Zhang, Z., et al.: Collaborative generative adversarial network for recommendation systems. In: Proceedings of the IEEE 35th International Conference on Data Engineering Workshops, pp. 161\u2013168 (2019)","DOI":"10.1109\/ICDEW.2019.00-16"},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 950\u2013958 (2019)","DOI":"10.1145\/3292500.3330989"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, J., Wang, J., et al.: GraphGAN: graph representation learning with generative adversarial nets. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11872"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Wang, J., Yu, L., Zhang, W., et al.: IRGAN: a minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 515\u2013524 (2017)","DOI":"10.1145\/3077136.3080786"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., et al.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165\u2013174 (2019)","DOI":"10.1145\/3331184.3331267"},{"key":"30_CR31","doi-asserted-by":"crossref","unstructured":"Wu, Y., DuBois, C., Zheng, A., et al.: Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 153\u2013162 (2016)","DOI":"10.1145\/2835776.2835837"},{"issue":"1","key":"30_CR32","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. learn. syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. learn. syst."},{"key":"30_CR33","doi-asserted-by":"crossref","unstructured":"Wang, S., Hu, L., Wang, Y., et al.: Sequential recommender systems: Challenges, progress and prospects. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 6332\u20136338 (2019)","DOI":"10.24963\/ijcai.2019\/883"},{"key":"30_CR34","doi-asserted-by":"crossref","unstructured":"Yuan, F., Yao, L., Benatallah, B.: Adversarial collaborative auto-encoder for top-n recommendation. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2019)","DOI":"10.1109\/IJCNN.2019.8851902"},{"key":"30_CR35","unstructured":"Yao, W., DuBois, C., Alice, Zheng., et al.: Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 153\u2013162 (2016)"},{"key":"30_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, W., Wang, B., Ye, J., et al.: PLASTIC: prioritize long and short-term information in top-n recommendation using adversarial training. In: Proceedings of the Proceedings of International Joint Conference on Artificial Intelligence, pp. 3676\u20133682 (2018)","DOI":"10.24963\/ijcai.2018\/511"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2390-4_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T18:18:55Z","timestamp":1714241935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2390-4_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819723898","9789819723904"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2390-4_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apweb-waim2023.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}