{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T22:30:33Z","timestamp":1792621833317,"version":"4.5.0"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"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":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>\n            Recommender systems commonly suffer from the long-standing data sparsity problem where insufficient user-item interaction data limits the systems\u2019 ability to make accurate recommendations. This problem can be alleviated using cross-domain recommendation techniques. In particular, in a cross-domain setting, knowledge sharing between domains permits improved effectiveness on the target domain. While recent cross-domain recommendation techniques used a pre-training configuration, we argue that such techniques lead to a low fine-tuning efficiency, especially when using large neural models. In recent language models,\n            <jats:italic>prompts<\/jats:italic>\n            have been used for parameter-efficient and time-efficient tuning of the models on the downstream tasks\u2014these prompts represent a tunable latent vector that permits to freeze the rest of the language model\u2019s parameters. To address the cross-domain recommendation task in an efficient manner, we propose a novel Personalised Graph Prompt-based Recommendation (PGPRec) framework, which leverages the efficiency benefits from prompt-tuning. In such a framework, we develop personalised and item-wise graph prompts based on relevant items to those items the user has interacted with. In particular, we apply Contrastive Learning to generate the pre-trained embeddings, to allow an increased generalisability in the pre-training stage, and to ensure an effective prompt-tuning stage. To evaluate the effectiveness of our PGPRec framework in a cross-domain setting, we conduct an extensive evaluation with the top-\n            <jats:italic>k<\/jats:italic>\n            recommendation task and perform a cold-start analysis. The obtained empirical results on four Amazon Review datasets show that our proposed PGPRec framework can reduce up to 74% of the tuned parameters with a competitive performance and achieves an 11.41% improved performance compared to the strongest baseline in a cold-start scenario.\n          <\/jats:p>","DOI":"10.1145\/3618298","type":"journal-article","created":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T11:30:03Z","timestamp":1693654203000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Contrastive Graph Prompt-tuning for Cross-domain Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7752-8650","authenticated-orcid":false,"given":"Zixuan","family":"Yi","sequence":"first","affiliation":[{"name":"University of Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4701-3223","authenticated-orcid":false,"given":"Iadh","family":"Ounis","sequence":"additional","affiliation":[{"name":"University of Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3143-279X","authenticated-orcid":false,"given":"Craig","family":"MacDonald","sequence":"additional","affiliation":[{"name":"University of Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the NeurIPS","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et\u00a0al. 2020. Language models are few-shot learners. In Proceedings of the NeurIPS."},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the EMNLP","author":"Chen Guanzheng","year":"2022","unstructured":"Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, and Shangsong Liang. 2022. Revisiting parameter-efficient tuning: Are we really there yet? In Proceedings of the EMNLP."},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the ICML","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the ICML."},{"key":"e_1_3_2_5_2","volume-title":"Proceedings of the RecSys","author":"Cui Qiang","year":"2020","unstructured":"Qiang Cui, Tao Wei, Yafeng Zhang, and Qing Zhang. 2020. HeroGRAPH: A heterogeneous graph framework for multi-target cross-domain recommendation. In Proceedings of the RecSys."},{"key":"e_1_3_2_6_2","article-title":"M6-Rec: Generative pre-trained language models are open-ended recommender systems","author":"Cui Zeyu","year":"2022","unstructured":"Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022. M6-Rec: Generative pre-trained language models are open-ended recommender systems. Retrieved from https:\/\/arXiv:2205.08084","journal-title":"Retrieved from https:\/\/arXiv:2205.08084"},{"key":"e_1_3_2_7_2","volume-title":"Proceedings of the NAACL","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the NAACL."},{"key":"e_1_3_2_8_2","article-title":"Prompt tuning for graph neural networks","author":"Fang Taoran","year":"2022","unstructured":"Taoran Fang, Yunchao Zhang, Yang Yang, and Chunping Wang. 2022. Prompt tuning for graph neural networks. Retrieved from https:\/\/arXiv:2209.15240","journal-title":"Retrieved from https:\/\/arXiv:2209.15240"},{"key":"e_1_3_2_9_2","volume-title":"Proceedings of the ACL","author":"Gao Tianyu","year":"2021","unstructured":"Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making pre-trained language models better few-shot learners. In Proceedings of the ACL."},{"key":"e_1_3_2_10_2","volume-title":"Proceedings of the RecSys","author":"Geng Shijie","year":"2022","unstructured":"Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as language processing (RLP): A unified pretrain, personalised prompt, and predict paradigm (P5). In Proceedings of the RecSys."},{"key":"e_1_3_2_11_2","volume-title":"Proceedings of the ICLR","author":"Gouk Henry","year":"2021","unstructured":"Henry Gouk, Timothy M. Hospedales, and Massimiliano Pontil. 2021. Distance-based regularisation of deep networks for fine-Tuning. In Proceedings of the ICLR."},{"key":"e_1_3_2_12_2","volume-title":"Proceedings of the ICLR","author":"He Junxian","year":"2022","unstructured":"Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2022. Towards a unified view of parameter-efficient transfer learning. In Proceedings of the ICLR."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_15_2","volume-title":"Proceedings of the of CIKM","author":"Hu Guangneng","year":"2018","unstructured":"Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. Conet: Collaborative cross networks for cross-domain recommendation. In Proceedings of the of CIKM."},{"issue":"4","key":"e_1_3_2_16_2","article-title":"Cumulated gain-based evaluation of IR techniques","volume":"20","author":"J\u00e4rvelin Kalervo","year":"2002","unstructured":"Kalervo J\u00e4rvelin and Jaana Kek\u00e4l\u00e4inen. 2002. Cumulated gain-based evaluation of IR techniques. TOIS 20, 4 (2002).","journal-title":"TOIS"},{"key":"e_1_3_2_17_2","article-title":"How can we know what language models know?","volume":"8","author":"Jiang Zhengbao","year":"2020","unstructured":"Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020. How can we know what language models know? TACL 8 (2020).","journal-title":"TACL"},{"key":"e_1_3_2_18_2","article-title":"Transformers are graph neural networks","volume":"7","author":"Joshi Chaitanya","year":"2020","unstructured":"Chaitanya Joshi. 2020. Transformers are graph neural networks. Gradient 7 (2020).","journal-title":"Gradient"},{"issue":"6","key":"e_1_3_2_19_2","article-title":"A survey on personalized recommendation techniques","volume":"2","author":"G. Saranya K.","year":"2014","unstructured":"Saranya K. G. and G. Sudha Sadasivam. 2014. A survey on personalized recommendation techniques. IJRITCC 2, 6 (2014).","journal-title":"IJRITCC"},{"key":"e_1_3_2_20_2","volume-title":"Proceedings of the ICLR","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the ICLR."},{"key":"e_1_3_2_21_2","volume-title":"Proceedings of the EMNLP","author":"Lester Brian","year":"2021","unstructured":"Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The power of scale for parameter-efficient prompt tuning. In Proceedings of the EMNLP."},{"key":"e_1_3_2_22_2","volume-title":"Proceedings of the ACL","author":"Li Xiang Lisa","year":"2021","unstructured":"Xiang Lisa Li and Percy Liang. 2021. Prefix-Tuning: Optimising continuous prompts for generation. In Proceedings of the ACL."},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the NeuIPS","author":"Liu Juncheng","year":"2021","unstructured":"Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, and Xiaokui Xiao. 2021. Eignn: Efficient infinite-depth graph neural networks. In Proceedings of the NeuIPS."},{"key":"e_1_3_2_24_2","volume-title":"Proceedings of the CIKM","author":"Liu Meng","year":"2020","unstructured":"Meng Liu, Jianjun Li, Guohui Li, and Peng Pan. 2020. Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In Proceedings of the CIKM."},{"issue":"9","key":"e_1_3_2_25_2","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2022","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2022. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys 55, 9 (2022).","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_26_2","article-title":"P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks","author":"Liu Xiao","year":"2022","unstructured":"Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022. P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. Proceedings of the ACL.","journal-title":"Proceedings of the ACL"},{"key":"e_1_3_2_27_2","article-title":"Contrastive self-supervised sequential recommendation with robust augmentation","author":"Liu Zhiwei","year":"2021","unstructured":"Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, and Caiming Xiong. 2021. Contrastive self-supervised sequential recommendation with robust augmentation. Retrieved from https:\/\/arXiv:2108.06479","journal-title":"Retrieved from https:\/\/arXiv:2108.06479"},{"key":"e_1_3_2_28_2","volume-title":"Proceedings of the SIGIR","author":"Liu Zhiwei","year":"2021","unstructured":"Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S. Yu. 2021. Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer. In Proceedings of the SIGIR."},{"key":"e_1_3_2_29_2","volume-title":"Proceedings of the ECIR","author":"Loni Babak","year":"2014","unstructured":"Babak Loni, Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Cross-domain collaborative filtering with factorisation machines. In Proceedings of the ECIR."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/343"},{"key":"e_1_3_2_31_2","volume-title":"Proceedings of the ECIR","author":"Manotumruksa Jarana","year":"2019","unstructured":"Jarana Manotumruksa, Dimitrios Rafailidis, Craig Macdonald, and Iadh Ounis. 2019. On cross-domain transfer in venue recommendation. In Proceedings of the ECIR."},{"key":"e_1_3_2_32_2","volume-title":"Proceedings of the CIKM","author":"Mao Kelong","year":"2021","unstructured":"Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. 2021. UltraGCN: Ultra simplification of graph convolutional networks for recommendation. In Proceedings of the CIKM."},{"issue":"3","key":"e_1_3_2_33_2","article-title":"Graph neural pre-training for enhancing recommendations using side information","volume":"41","author":"Meng Zaiqiao","year":"2023","unstructured":"Zaiqiao Meng, Siwei Liu, Craig Macdonald, and Iadh Ounis. 2023. Graph neural pre-training for enhancing recommendations using side information. TOIS 41, 3 (2023).","journal-title":"TOIS"},{"issue":"4","key":"e_1_3_2_34_2","article-title":"Improving top-n recommendation for cold-start users via cross-domain information","volume":"9","author":"Mirbakhsh Nima","year":"2015","unstructured":"Nima Mirbakhsh and Charles X. Ling. 2015. Improving top-n recommendation for cold-start users via cross-domain information. TKDD 9, 4 (2015).","journal-title":"TKDD"},{"key":"e_1_3_2_35_2","article-title":"Representation learning with contrastive predictive coding","author":"Oord Aaron van den","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. Retrieved from https:\/\/arXiv:1807.03748","journal-title":"Retrieved from https:\/\/arXiv:1807.03748"},{"key":"e_1_3_2_36_2","volume-title":"Proceedings of the NAACL","author":"Qin Guanghui","year":"2021","unstructured":"Guanghui Qin and Jason Eisner. 2021. Learning how to ask: Querying LMs with mixtures of soft prompts. In Proceedings of the NAACL."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_38_2","article-title":"Language models are unsupervised multitask learners","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et\u00a0al. 2019. Language models are unsupervised multitask learners. OpenAI blog (2019). https:\/\/d4mucfpksywv.cloudfront.net\/better-language-models\/language-models.pdf","journal-title":"OpenAI blog"},{"key":"e_1_3_2_39_2","volume-title":"Proceedings of the 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 Proceedings of the UAI."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"e_1_3_2_41_2","volume-title":"Proceedings of the SIGIR Forum","author":"Sakai Tetsuya","year":"2021","unstructured":"Tetsuya Sakai. 2021. On Fuhr\u2019s guideline for IR evaluation. In Proceedings of the SIGIR Forum."},{"key":"e_1_3_2_42_2","volume-title":"Proceedings of the EACL","author":"Schick Timo","year":"2020","unstructured":"Timo Schick and Hinrich Sch\u00fctze. 2020. Exploiting cloze questions for few shot text classification and natural language inference. In Proceedings of the EACL."},{"key":"e_1_3_2_43_2","article-title":"A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability","volume":"15","author":"Schuirmann Donald J.","year":"1987","unstructured":"Donald J. Schuirmann. 1987. A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J. Pharmacok. Biopharm. 15 (1987).","journal-title":"J. Pharmacok. Biopharm."},{"key":"e_1_3_2_44_2","article-title":"Autoprompt: Eliciting knowledge from language models with automatically generated prompts","author":"Shin Taylor","year":"2020","unstructured":"Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. Retrieved from https:\/\/arXiv:2010.15980","journal-title":"Retrieved from https:\/\/arXiv:2010.15980"},{"issue":"2","key":"e_1_3_2_45_2","article-title":"The top ten: Best practice recommendations for aphasia","volume":"31","author":"Simmons-Mackie Nina","year":"2017","unstructured":"Nina Simmons-Mackie, Linda Worrall, Laura L. Murray, Pam Enderby, Miranda L. Rose, Eun Jin Paek, and Anu Klippi. 2017. The top ten: Best practice recommendations for aphasia. Aphasiology 31, 2 (2017).","journal-title":"Aphasiology"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_47_2","volume-title":"Proceedings of the KDD","author":"Sun Mingchen","year":"2022","unstructured":"Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022. GPPT: Graph pre-training and prompt tuning to generalize graph neural networks. In Proceedings of the KDD."},{"key":"e_1_3_2_48_2","article-title":"Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization","volume":"55","author":"Tang Jun","year":"2016","unstructured":"Jun Tang, Haiqun Jin, Shoubiao Tan, and Dong Liang. 2016. Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization. Image Vision Comput. 55 (2016).","journal-title":"Image Vision Comput."},{"key":"e_1_3_2_49_2","volume-title":"Proceedings of the NeuIPS","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the NeuIPS."},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the ICLR","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the ICLR."},{"key":"e_1_3_2_51_2","volume-title":"Proceedings of the CogMI","author":"Wang Chen","year":"2021","unstructured":"Chen Wang, Yueqing Liang, Zhiwei Liu, Tao Zhang, and S. Yu Philip. 2021. Pre-training graph neural network for cross-domain recommendation. In Proceedings of the CogMI."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"issue":"4","key":"e_1_3_2_53_2","article-title":"Cross-domain recommendation based on sentiment analysis and latent feature mapping","volume":"22","author":"Wang Yongpeng","year":"2020","unstructured":"Yongpeng Wang, Hong Yu, Guoyin Wang, and Yongfang Xie. 2020. Cross-domain recommendation based on sentiment analysis and latent feature mapping. Entropy 22, 4 (2020).","journal-title":"Entropy"},{"key":"e_1_3_2_54_2","volume-title":"Proceedings of the ACL","author":"Wu Hui","year":"2022","unstructured":"Hui Wu and Xiaodong Shi. 2022. Adversarial soft prompt tuning for cross-domain sentiment analysis. In Proceedings of the ACL."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_56_2","article-title":"Personalized prompts for sequential recommendation","author":"Wu Yiqing","year":"2022","unstructured":"Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xu Zhang, Leyu Lin, and Qing He. 2022. Personalized prompts for sequential recommendation. Retrieved from https:\/\/arXiv:2205.09666","journal-title":"Retrieved from https:\/\/arXiv:2205.09666"},{"key":"e_1_3_2_57_2","article-title":"UPRec: User-aware pre-training for recommender systems","author":"Xiao Chaojun","year":"2021","unstructured":"Chaojun Xiao, Ruobing Xie, Yuan Yao, Zhiyuan Liu, Maosong Sun, Xu Zhang, and Leyu Lin. 2021. UPRec: User-aware pre-training for recommender systems. Retrieved from https:\/\/arXiv:2102.10989","journal-title":"Retrieved from https:\/\/arXiv:2102.10989"},{"key":"e_1_3_2_58_2","volume-title":"Proceedings of the ICDE","author":"Xie Xu","year":"2022","unstructured":"Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In Proceedings of the ICDE."},{"key":"e_1_3_2_59_2","volume-title":"Proceedings of the ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the ICLR."},{"key":"e_1_3_2_60_2","volume-title":"Proceedings of the WWW","author":"Yang Zhen","year":"2022","unstructured":"Zhen Yang, Ming Ding, Bin Xu, Hongxia Yang, and Jie Tang. 2022. STAM: A spatiotemporal aggregation method for graph neural network-based recommendation. In Proceedings of the WWW."},{"key":"e_1_3_2_61_2","article-title":"Self-supervised learning for recommender systems: A survey","author":"Yu Junliang","year":"2023","unstructured":"Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, and Zi Huang. 2023. Self-supervised learning for recommender systems: A survey. TKDE (2023).","journal-title":"TKDE"},{"key":"e_1_3_2_62_2","volume-title":"Proceedings of the SIGIR","author":"Yuan Fajie","year":"2020","unstructured":"Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, and Liguang Zhang. 2020. Parameter-efficient transfer from sequential behaviours for user modeling and recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_63_2","volume-title":"Proceedings of the IJCAI","author":"Yuan Feng","year":"2019","unstructured":"Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns. In Proceedings of the IJCAI."},{"issue":"2","key":"e_1_3_2_64_2","article-title":"A survey on cross-domain recommendation: taxonomies, methods, and future directions","volume":"41","author":"Zang Tianzi","year":"2022","unstructured":"Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, and Jiadi Yu. 2022. A survey on cross-domain recommendation: taxonomies, methods, and future directions. TOIS 41, 2 (2022).","journal-title":"TOIS"},{"key":"e_1_3_2_65_2","article-title":"Knowledge transfer via pre-training for recommendation: A review and prospect","volume":"4","author":"Zeng Zheni","year":"2021","unstructured":"Zheni Zeng, Chaojun Xiao, Yuan Yao, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun. 2021. Knowledge transfer via pre-training for recommendation: A review and prospect. Front. Big Data 4 (2021).","journal-title":"Front. Big Data"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358166"},{"key":"e_1_3_2_67_2","volume-title":"Proceedings of the WSDM","author":"Zhu Yongchun","year":"2022","unstructured":"Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. 2022. Personalized transfer of user preferences for cross-domain recommendation. In Proceedings of the WSDM."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3618298","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3618298","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:46Z","timestamp":1750291426000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3618298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,11]]},"references-count":66,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3,31]]}},"alternative-id":["10.1145\/3618298"],"URL":"https:\/\/doi.org\/10.1145\/3618298","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,11]]},"assertion":[{"value":"2022-11-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-16","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}