{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:45:10Z","timestamp":1777567510615,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T00:00:00Z","timestamp":1625961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,11]]},"DOI":"10.1145\/3404835.3463089","type":"proceedings-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T16:57:07Z","timestamp":1626886627000},"page":"1783-1787","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["Sequential Recommendation for Cold-start Users with Meta Transitional Learning"],"prefix":"10.1145","author":[{"given":"Jianling","family":"Wang","sequence":"first","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaize","family":"Ding","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Caverlee","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"DCDIR: A deep cross-domain recommendation system for cold start users in insurance domain. In SIGIR.","author":"Bi Ye","year":"2020","unstructured":"Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, and Jing Xiao. 2020. DCDIR: A deep cross-domain recommendation system for cold start users in insurance domain. In SIGIR."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Mingyang Chen Wen Zhang Wei Zhang Qiang Chen and Huajun Chen. 2019. Meta relational learning for few-shot link prediction in knowledge graphs. In EMNLP.","DOI":"10.18653\/v1\/D19-1431"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Kaize Ding Jianling Wang Jundong Li Kai Shu Chenghao Liu and Huan Liu. 2020. Graph prototypical networks for few-shot learning on attributed networks. In CIKM.","DOI":"10.1145\/3340531.3411922"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449922"},{"key":"e_1_3_2_1_5_1","unstructured":"Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Zeno Gantner Lucas Drumond Christoph Freudenthaler Steffen Rendle and Lars Schmidt-Thieme. 2010. Learning attribute-to-feature mappings for cold-start recommendations. In ICDM.","DOI":"10.1109\/ICDM.2010.129"},{"key":"e_1_3_2_1_7_1","unstructured":"Ruining He Wang-Cheng Kang and Julian McAuley. 2017a. Translation-based recommendation. In RecSys."},{"key":"e_1_3_2_1_8_1","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017b. Neural collaborative filtering. In WWW."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Bal\u00e1zs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In CIKM.","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_1_10_1","volume-title":"Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"SeongKu Kang Junyoung Hwang Dongha Lee and Hwanjo Yu. 2019. Semi-supervised learning for cross-domain recommendation to cold-start users. In CIKM.","DOI":"10.1145\/3357384.3357914"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM.","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_13_1","unstructured":"Gregory Koch Richard Zemel and Ruslan Salakhutdinov. 2015. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop."},{"key":"e_1_3_2_1_14_1","unstructured":"Hoyeop Lee Jinbae Im Seongwon Jang Hyunsouk Cho and Sehee Chung. 2019. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. In KDD."},{"key":"e_1_3_2_1_15_1","unstructured":"Jingjing Li Mengmeng Jing Ke Lu Lei Zhu Yang Yang and Zi Huang. 2019. From zero-shot learning to cold-start recommendation. In AAAI."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Julian McAuley Christopher Targett Qinfeng Shi and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR.","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Nima Mirbakhsh and Charles X Ling. 2015. Improving top-n recommendation for cold-start users via cross-domain information. In TKDD.","DOI":"10.1145\/2724720"},{"key":"e_1_3_2_1_18_1","unstructured":"Alex Nichol Joshua Achiam and John Schulman. 2018. On first-order meta-learning algorithms. In arXiv preprint arXiv:1803.02999."},{"key":"e_1_3_2_1_19_1","unstructured":"Feiyang Pan Shuokai Li Xiang Ao Pingzhong Tang and Qing He. 2019. Warm up cold-start advertisements: Improving ctr predictions via learning to learn id embeddings. In SIGIR."},{"key":"e_1_3_2_1_20_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. In UAI.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle Christoph Freudenthaler and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW.","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_22_1","unstructured":"Adam Santoro Sergey Bartunov Matthew Botvinick Daan Wierstra and Timothy Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In ICML."},{"key":"e_1_3_2_1_23_1","unstructured":"Jake Snell Kevin Swersky and Richard Zemel. 2017. Prototypical networks for few-shot learning. In NeurIPS."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. CIKM.","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM.","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_1_26_1","unstructured":"Aaron Van den Oord Sander Dieleman and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In NeurIPS."},{"key":"e_1_3_2_1_27_1","unstructured":"Manasi Vartak Arvind Thiagarajan Conrado Miranda Jeshua Bratman and Hugo Larochelle. 2017. A meta-learning perspective on cold-start recommendations for items. In NeurIPS."},{"key":"e_1_3_2_1_28_1","unstructured":"Oriol Vinyals Charles Blundell Timothy Lillicrap Daan Wierstra et al. 2016. Matching networks for one shot learning. In NeurIPS."},{"key":"e_1_3_2_1_29_1","volume-title":"Dropoutnet: Addressing cold start in recommender systems. In NeurIPS.","author":"Volkovs Maksims","year":"2017","unstructured":"Maksims Volkovs, Guangwei Yu, and Tomi Poutanen. 2017. Dropoutnet: Addressing cold start in recommender systems. In NeurIPS."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Jianling Wang Kaize Ding Liangjie Hong Huan Liu and James Caverlee. 2020. Next-item recommendation with sequential hypergraphs. In SIGIR.","DOI":"10.1145\/3397271.3401133"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Jianling Wang Kaize Ding Ziwei Zhu and James Caverlee. 2021. Session-based Recommendation with Hypergraph Attention Networks. In SDM.","DOI":"10.1137\/1.9781611976700.10"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In SIGIR.","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_33_1","unstructured":"Tianxin Wei Ziwei Wu Ruirui Li Ziniu Hu Fuli Feng Xiangnan He Yizhou Sun and Wei Wang. 2020. Fast Adaptation for Cold-start Collaborative Filtering with Meta-learning. ICDM."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Shu Wu Yuyuan Tang Yanqiao Zhu Liang Wang Xing Xie and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In AAAI.","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_1_35_1","unstructured":"Jianwen Yin Chenghao Liu Weiqing Wang Jianling Sun and Steven CH Hoi. 2020. Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling. In KDD."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Fajie Yuan Alexandros Karatzoglou Ioannis Arapakis Joemon M Jose and Xiangnan He. 2019. A Simple Convolutional Generative Network for Next Item Recommendation. In WSDM.","DOI":"10.1145\/3289600.3290975"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Ziwei Zhu Shahin Sefati Parsa Saadatpanah and James Caverlee. 2020. Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. In SIGIR.","DOI":"10.1145\/3397271.3401178"}],"event":{"name":"SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Virtual Event Canada","acronym":"SIGIR '21","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3463089","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3404835.3463089","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:30Z","timestamp":1750191510000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3463089"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,11]]},"references-count":37,"alternative-id":["10.1145\/3404835.3463089","10.1145\/3404835"],"URL":"https:\/\/doi.org\/10.1145\/3404835.3463089","relation":{},"subject":[],"published":{"date-parts":[[2021,7,11]]},"assertion":[{"value":"2021-07-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}