{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:13:24Z","timestamp":1757312004773,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819724208"},{"type":"electronic","value":"9789819724215"}],"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\/978-981-97-2421-5_25","type":"book-chapter","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:01:48Z","timestamp":1715414508000},"page":"375-389","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Wasserstein Adversarial Variational Autoencoder for\u00a0Sequential Recommendation"],"prefix":"10.1007","author":[{"given":"Wenbiao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianjin","family":"Rong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingli","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,12]]},"reference":[{"key":"25_CR1","unstructured":"Adler, J., Lunz, S.: Banach wasserstein gan. Adv. Neural Inf. Process. Syst. (2018)"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Chae, D.K., Kang, J.S., Kim, S.W.: CFGAN: a generic collaborative filtering framework based on generative adversarial networks. In: CIKM, pp. 137\u2013146 (2018)","DOI":"10.1145\/3269206.3271743"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Sequential recommendation with user memory networks. In: WSDM, pp. 108\u2013116 (2018)","DOI":"10.1145\/3159652.3159668"},{"key":"25_CR4","unstructured":"Chen, Z., Liu, P.: Towards better data augmentation using wasserstein distance in variational auto-encoder. arXiv preprint arXiv:2109.14795 (2021)"},{"key":"25_CR5","unstructured":"Ezukwoke, K., Hoayek, A., Batton-Hubert, M., Boucher, X.: GCVAE: generalized-controllable variational autoencoder (2022)"},{"key":"25_CR6","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)"},{"key":"25_CR7","unstructured":"Higgins, I., et al.: Beta-vae: learning basic visual concepts with a constrained variational framework (2016)"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: ICDM, pp. 197\u2013206. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference, pp. 689\u2013698 (2018)","DOI":"10.1145\/3178876.3186150"},{"key":"25_CR10","unstructured":"Mescheder, L., Nowozin, S., Geiger, A.: Adversarial variational bayes: unifying variational autoencoders and generative adversarial networks. In: ICML, pp. 2391\u20132400. PMLR (2017)"},{"key":"25_CR11","unstructured":"Miladinovi\u0107, D., Shridhar, K., Jain, K., Paulus, M.B., Buhmann, J.M., Allen, C.: Learning to drop out: an adversarial approach to training sequence VAEs (2022)"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Sachdeva, N., Manco, G., Ritacco, E., Pudi, V.: Sequential variational autoencoders for collaborative filtering. In: WSDM, pp. 600\u2013608 (2019)","DOI":"10.1145\/3289600.3291007"},{"key":"25_CR13","unstructured":"Sohn, K., Lee, H.: Learning structured output representation using deep conditional generative models. Adv. Neural Inf. Process. Syst. (2015)"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Sun, F., et al.: Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: CIKM, pp. 1441\u20131450 (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: WSDM, pp. 565\u2013573 (2018)","DOI":"10.1145\/3159652.3159656"},{"key":"25_CR16","unstructured":"Van Den\u00a0Oord, A., Kalchbrenner, N.: Pixel recurrent neural networks. In: International Conference on Machine Learning, pp. 1747\u20131756. PMLR (2016)"},{"key":"25_CR17","unstructured":"Wang, J., et al.: A minimax game for unifying generative and discriminative information retrieval models. In: SIGIR Proceedings (2018)"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z., Orgun, M.A.: Sequential Recommender Systems - Challenges, Progress and Prospects, pp. 6332\u20136338 (2019)","DOI":"10.24963\/ijcai.2019\/883"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Normative modeling via conditional variational autoencoder and adversarial learning to identify brain dysfunction in Alzheimer\u2019s disease (2022)","DOI":"10.1109\/ISBI53787.2023.10230377"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Xie, Z., Liu, C., Zhang, Y., Lu, H., Wang, D., Ding, Y.: Adversarial and contrastive variational autoencoder for sequential recommendation. In: Proceedings of the Web Conference 2021, pp. 449\u2013459 (2021)","DOI":"10.1145\/3442381.3449873"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Yu, X., Zhang, X., Cao, Y., Xia, M.: Vaegan: a collaborative filtering framework based on adversarial variational autoencoders. In: Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19 (2019)","DOI":"10.24963\/ijcai.2019\/584"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, S., Song, J., Ermon, S.: Infovae: Balancing learning and inference in variational autoencoders, pp. 5885\u20135892 (2019)","DOI":"10.1609\/aaai.v33i01.33015885"}],"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\/978-981-97-2421-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:07:15Z","timestamp":1715414835000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2421-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819724208","9789819724215"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2421-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"12 May 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"}}]}}