{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T21:41:12Z","timestamp":1786138872632,"version":"3.56.0"},"reference-count":73,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"vor","delay-in-days":354,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success in the field of Natural Language Processing (NLP) by learning universal representations on large corpora in a self-supervised manner. The pre-trained models and the learned representations can be beneficial to a series of downstream NLP tasks. This training paradigm has recently been adapted to the recommendation domain and is considered a promising approach by both academia and industry. In this paper, we systematically investigate how to extract and transfer knowledge from pre-trained models learned by different PLM-related training paradigms to improve recommendation performance from various perspectives, such as generality, sparsity, efficiency and effectiveness. Specifically, we propose a comprehensive taxonomy to divide existing PLM-based recommender systems w.r.t. their training strategies and objectives. Then, we analyze and summarize the connection between PLM-based training paradigms and different input data types for recommender systems. Finally, we elaborate on open issues and future research directions in this vibrant field.<\/jats:p>","DOI":"10.1162\/tacl_a_00619","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T18:59:00Z","timestamp":1702666740000},"page":"1553-1571","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":88,"title":["Pre-train, Prompt, and Recommendation: A Comprehensive Survey of Language Modeling Paradigm Adaptations in Recommender Systems"],"prefix":"10.1162","volume":"11","author":[{"given":"Peng","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science Norwegian University of Science and Technology, Norway. peng.liu@ntnu.no"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lemei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science Norwegian University of Science and Technology, Norway. lemei.zhang@ntnu.no"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jon Atle","family":"Gulla","sequence":"additional","affiliation":[{"name":"Department of Computer Science Norwegian University of Science and Technology, Norway. jon.atle.gulla@ntnu.no"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"2023122118393692600_bib1","first-page":"642","article-title":"UniLMv2: Pseudo-masked language models for unified language model pre-training","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Bao","year":"2020"},{"key":"2023122118393692600_bib2","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Advances in Neural Information Processing Systems","author":"Brown","year":"2020"},{"issue":"3","key":"2023122118393692600_bib3","doi-asserted-by":"publisher","first-page":"3239","DOI":"10.1109\/TKDE.2021.3119619","article-title":"User-specific adaptive fine-tuning for cross-domain recommendations","volume":"35","author":"Chen","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"3","key":"2023122118393692600_bib4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3570640","article-title":"A unified multi-task learning framework for multi-goal conversational recommender systems","volume":"41","author":"Deng","year":"2023","journal-title":"ACM Transactions on Information Systems"},{"key":"2023122118393692600_bib5","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.18653\/v1\/N19-1423","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin","year":"2019"},{"key":"2023122118393692600_bib6","first-page":"201","article-title":"Why does unsupervised pre-training help deep learning?","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","author":"Erhan","year":"2010"},{"key":"2023122118393692600_bib7","article-title":"Chat-REC: Towards interactive and explainable LLMs-augmented recommender system","author":"Gao","year":"2023","journal-title":"arXiv preprint arXiv:2303.14524v2"},{"key":"2023122118393692600_bib8","doi-asserted-by":"publisher","first-page":"244","DOI":"10.18653\/v1\/2022.acl-long.20","article-title":"Improving personalized explanation generation through visualization","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Geng","year":"2022"},{"key":"2023122118393692600_bib9","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1145\/3485447.3511937","article-title":"Path language modeling over knowledge graphs for explainable recommendation","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Geng","year":"2022"},{"key":"2023122118393692600_bib10","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1145\/3523227.3546767","article-title":"Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5)","volume-title":"Proceedings of the 16th ACM Conference on Recommender Systems","author":"Geng","year":"2022"},{"key":"2023122118393692600_bib11","article-title":"VIP5: Towards multimodal foundation models for recommendation","author":"Geng","year":"2023","journal-title":"arXiv preprint arXiv:2305.14302v1"},{"key":"2023122118393692600_bib12","article-title":"Automated prompting for non-overlapping cross-domain sequential recommendation","author":"Guo","year":"2023","journal-title":"arXiv preprint arXiv:2304.04218v1"},{"key":"2023122118393692600_bib13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1145\/3404835.3462939","article-title":"ReXPlug: Explainable recommendation using plug-and-play language model","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Hada","year":"2021"},{"key":"2023122118393692600_bib14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3524610.3527897","article-title":"PTM4Tag: Sharpening tag recommendation of stack overflow posts with pre-trained models","volume-title":"Proceedings of the 30th IEEE\/ACM International Conference on Program Comprehension","author":"He","year":"2022"},{"key":"2023122118393692600_bib15","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1145\/3543507.3583434","article-title":"Learning vector-quantized item representation for transferable sequential recommenders","author":"Hou","year":"2023"},{"key":"2023122118393692600_bib16","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1145\/3534678.3539381","article-title":"Towards universal sequence representation learning for recommender systems","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Hou","year":"2022"},{"key":"2023122118393692600_bib17","article-title":"Learning large-scale universal user representation with sparse mixture of experts","volume-title":"First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML 2022","author":"Jiang","year":"2022"},{"key":"2023122118393692600_bib18","doi-asserted-by":"publisher","first-page":"3425","DOI":"10.18653\/v1\/2021.emnlp-main.275","article-title":"APIRecX: Cross-library API recommendation via pre-trained language model","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","author":"Kang","year":"2021"},{"key":"2023122118393692600_bib19","article-title":"GPT4Rec: A generative framework for personalized recommendation and user interests interpretation","volume-title":"SIGIR 2023 Workshop on eCommerce","author":"Li","year":"2023"},{"key":"2023122118393692600_bib20","doi-asserted-by":"publisher","first-page":"4947","DOI":"10.18653\/v1\/2021.acl-long.383","article-title":"Personalized transformer for explainable recommendation","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Li","year":"2021"},{"issue":"4","key":"2023122118393692600_bib21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3580488","article-title":"Personalized prompt learning for explainable recommendation","volume":"41","author":"Li","year":"2023","journal-title":"ACM Transactions on Information Systems"},{"issue":"6","key":"2023122118393692600_bib22","doi-asserted-by":"publisher","first-page":"103067","DOI":"10.1016\/j.ipm.2022.103067","article-title":"Self-supervised learning for conversational recommendation","volume":"59","author":"Li","year":"2022","journal-title":"Information Processing & Management"},{"key":"2023122118393692600_bib23","article-title":"Is ChatGPT a good recommender? A preliminary study","author":"Liu","year":"2023","journal-title":"arXiv preprint arXiv:2304.10149v2"},{"issue":"9","key":"2023122118393692600_bib24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu","year":"2023","journal-title":"ACM Computing Surveys"},{"key":"2023122118393692600_bib25","first-page":"2823","article-title":"Boosting deep CTR prediction with a plug-and-play pre-trainer for news recommendation","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"Liu","year":"2022"},{"issue":"3","key":"2023122118393692600_bib26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3568953","article-title":"Graph neural pre-training for recommendation with side information","volume":"41","author":"Liu","year":"2023","journal-title":"ACM Transactions on Information Systems"},{"key":"2023122118393692600_bib27","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu","year":"2019","journal-title":"arXiv preprint arXiv:1907.11692v1"},{"issue":"6","key":"2023122118393692600_bib28","first-page":"5879","article-title":"Graph self-supervised learning: A survey","volume":"35","author":"Liu","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2023122118393692600_bib29","doi-asserted-by":"publisher","first-page":"2853","DOI":"10.1145\/3474085.3475709","article-title":"Pre-training graph transformer with multimodal side information for recommendation","volume-title":"Proceedings of the 29th ACM International Conference on Multimedia","author":"Liu","year":"2021"},{"key":"2023122118393692600_bib30","doi-asserted-by":"publisher","first-page":"5530","DOI":"10.24963\/ijcai.2022\/773","article-title":"Vision-and-language pretrained models: A survey","volume-title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22","author":"Long","year":"2022"},{"key":"2023122118393692600_bib31","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.18653\/v1\/2023.findings-acl.217","article-title":"Multimodal recommendation dialog with subjective preference: A new challenge and benchmark","volume-title":"Findings of the Association for Computational Linguistics: ACL 2023","author":"Long","year":"2023"},{"key":"2023122118393692600_bib32","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.18653\/v1\/2020.findings-emnlp.154","article-title":"RecoBERT: A catalog language model for text-based recommendations","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2020","author":"Malkiel","year":"2020"},{"key":"2023122118393692600_bib33","doi-asserted-by":"publisher","first-page":"14784","DOI":"10.1109\/CVPR52729.2023.01420","article-title":"Language-guided music recommendation for video via prompt analogies","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"McKee","year":"2023"},{"key":"2023122118393692600_bib34","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1145\/3383313.3412249","article-title":"What does BERT know about books, movies and music? Probing BERT for conversational recommendation","volume-title":"Proceedings of the 14th ACM Conference on Recommender Systems","author":"Penha","year":"2020"},{"key":"2023122118393692600_bib35","doi-asserted-by":"publisher","first-page":"5203","DOI":"10.18653\/v1\/2021.naacl-main.410","article-title":"Learning how to ask: Querying LMs with mixtures of soft prompts","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Qin","year":"2021"},{"issue":"10","key":"2023122118393692600_bib36","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","article-title":"Pre-trained models for natural language processing: A survey","volume":"63","author":"Qiu","year":"2020","journal-title":"Science China Technological Sciences"},{"key":"2023122118393692600_bib37","doi-asserted-by":"publisher","first-page":"4320","DOI":"10.1609\/aaai.v35i5.16557","article-title":"U-BERT: Pre-training user representations for improved recommendation","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Qiu","year":"2021"},{"key":"2023122118393692600_bib38","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1145\/3460231.3474268","article-title":"ProtoCF: Prototypical collaborative filtering for few-shot recommendation","volume-title":"Proceedings of the 15th ACM Conference on Recommender Systems","author":"Sankar","year":"2021"},{"key":"2023122118393692600_bib39","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1109\/CVPRW56347.2022.00249","article-title":"OutfitTransformer: Outfit representations for fashion recommendation","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","author":"Sarkar","year":"2022"},{"key":"2023122118393692600_bib40","doi-asserted-by":"publisher","first-page":"5953","DOI":"10.24963\/ijcai.2019\/825","article-title":"Pre-training of graph augmented transformers for medication recommendation","volume-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19","author":"Shang","year":"2019"},{"key":"2023122118393692600_bib41","doi-asserted-by":"publisher","first-page":"4596","DOI":"10.1609\/aaai.v37i4.25582","article-title":"Scaling law for recommendation models: Towards general-purpose user representations","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Shin","year":"2023"},{"key":"2023122118393692600_bib42","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-3-030-99739-7_26","article-title":"Zero-shot recommendation as language modeling","volume-title":"Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10\u201314, 2022, Proceedings, Part II","author":"Sileo","year":"2022"},{"key":"2023122118393692600_bib43","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1145\/3460231.3474255","article-title":"Transformers4Rec: Bridging the gap between nlp and sequential\/session-based recommendation","volume-title":"Proceedings of the 15th ACM Conference on Recommender Systems","author":"de Souza Pereira Moreira","year":"2021"},{"key":"2023122118393692600_bib44","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1145\/3357384.3357895","article-title":"BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Sun","year":"2019"},{"issue":"1","key":"2023122118393692600_bib45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528667","article-title":"Curriculum pre-training heterogeneous subgraph transformer for top-n recommendation","volume":"41","author":"Wang","year":"2023","journal-title":"ACM Transactions on Information Systems"},{"key":"2023122118393692600_bib46","first-page":"489","article-title":"RecInDial: A unified framework for conversational recommendation with pretrained language models","volume-title":"Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Wang","year":"2022"},{"key":"2023122118393692600_bib73","article-title":"Improving conversational recommender system via contextual and time-aware modeling with less domain-specific knowledge","volume-title":"arXiv preprint arXiv:2209.11386v1","author":"Wang","year":"2022"},{"key":"2023122118393692600_bib47","doi-asserted-by":"publisher","first-page":"3094","DOI":"10.18653\/v1\/2022.findings-emnlp.225","article-title":"Learning when and what to quote: A quotation recommender system with mutual promotion of recommendation and generation","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2022","author":"Wang","year":"2022"},{"key":"2023122118393692600_bib48","doi-asserted-by":"publisher","DOI":"10.1145\/3594633","article-title":"Quotation recommendation for multi-party online conversations based on semantic and topic fusion","author":"Wang","year":"2023","journal-title":"ACM Transactions on Information Systems"},{"key":"2023122118393692600_bib49","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1145\/3534678.3539382","article-title":"Towards unified conversational recommender systems via knowledge-enhanced prompt learning","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Wang","year":"2022"},{"key":"2023122118393692600_bib50","doi-asserted-by":"publisher","first-page":"page 1652\u2013page 1656","DOI":"10.1145\/3404835.3463069","article-title":"Empowering news recommendation with pre-trained language models","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Chuhan","year":"2021"},{"key":"2023122118393692600_bib51","first-page":"2560","article-title":"MM-Rec: Visiolinguistic model empowered multimodal news recommendation","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Chuhan","year":"2022"},{"key":"2023122118393692600_bib52","article-title":"Personalized prompts for sequential recommendation","author":"Yiqing","year":"2022","journal-title":"arXiv preprint arXiv:2205.09666v2"},{"key":"2023122118393692600_bib53","article-title":"UPRec: User-aware pre-training for recommender systems","author":"Xiao","year":"2021","journal-title":"arXiv preprint arXiv:2102.10989v1"},{"key":"2023122118393692600_bib54","doi-asserted-by":"publisher","first-page":"4215","DOI":"10.1145\/3534678.3539120","article-title":"Training large-scale news recommenders with pretrained language models in the loop","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Xiao","year":"2022"},{"key":"2023122118393692600_bib55","doi-asserted-by":"publisher","first-page":"13816","DOI":"10.1609\/aaai.v37i11.26618","article-title":"Factual and informative review generation for explainable recommendation","author":"Xie","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2023122118393692600_bib56","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1145\/3477495.3531714","article-title":"Rethinking reinforcement learning for recommendation: A prompt perspective","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Xin","year":"2022"},{"key":"2023122118393692600_bib57","doi-asserted-by":"publisher","first-page":"38","DOI":"10.18653\/v1\/2022.findings-naacl.4","article-title":"Improving conversational recommendation systems\u2019 quality with context-aware item meta-information","volume-title":"Findings of the Association for Computational Linguistics: NAACL 2022","author":"Yang","year":"2022"},{"key":"2023122118393692600_bib58","doi-asserted-by":"publisher","first-page":"839","DOI":"10.18653\/v1\/2022.naacl-main.61","article-title":"GRAM: Fast fine-tuning of pre-trained language models for content-based collaborative filtering","volume-title":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Yang","year":"2022"},{"key":"2023122118393692600_bib59","volume-title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding","author":"Yang","year":"2019"},{"key":"2023122118393692600_bib60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2023.3282907","article-title":"Self-supervised learning for recommender systems: A survey","author":"Junliang","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2023122118393692600_bib61","doi-asserted-by":"publisher","first-page":"5478","DOI":"10.18653\/v1\/2022.emnlp-main.368","article-title":"Tiny-NewsRec: Effective and efficient PLM-based news recommendation","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing","author":"Yang","year":"2022"},{"key":"2023122118393692600_bib62","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1145\/3366423.3380116","article-title":"Future data helps training: Modeling future contexts for session-based recommendation","volume-title":"Proceedings of The Web Conference 2020","author":"Yuan","year":"2020"},{"key":"2023122118393692600_bib63","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1145\/3397271.3401156","article-title":"Parameter-efficient transfer from sequential behaviors for user modeling and recommendation","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yuan","year":"2020"},{"key":"2023122118393692600_bib64","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.602071","article-title":"Knowledge transfer via pre-training for recommendation: A review and prospect","volume":"4","author":"Zeng","year":"2021","journal-title":"Frontiers in big Data"},{"key":"2023122118393692600_bib65","doi-asserted-by":"publisher","first-page":"3356","DOI":"10.24963\/ijcai.2021\/462","article-title":"UNBERT: User-news matching bert for news recommendation","volume-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21","author":"Qi","year":"2021"},{"key":"2023122118393692600_bib66","doi-asserted-by":"publisher","first-page":"5597","DOI":"10.1145\/3580305.3599921","article-title":"TwHIN-BERT: A socially-enriched pre-trained language model for multilingual tweet representations at twitter","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Zhang","year":"2023"},{"key":"2023122118393692600_bib67","article-title":"Language models as recommender systems: Evaluations and limitations","volume-title":"NeurIPS 2021 Workshop on I (Still) Can\u2019t Believe It\u2019s Not Better","author":"Zhang","year":"2021"},{"key":"2023122118393692600_bib68","doi-asserted-by":"publisher","first-page":"3684","DOI":"10.1145\/3511808.3557106","article-title":"KEEP: An industrial pre-training framework for online recommendation via knowledge extraction and plugging","volume-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","author":"Zhang","year":"2022"},{"key":"2023122118393692600_bib69","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1145\/3539618.3591752","article-title":"Prompt learning for news recommendation","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhang","year":"2023"},{"key":"2023122118393692600_bib70","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1145\/3477495.3532054","article-title":"RESETBERT4Rec: A pre-training model integrating time and user historical behavior for sequential recommendation","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhao","year":"2022"},{"key":"2023122118393692600_bib71","article-title":"Spatial autoregressive coding for graph neural recommendation","author":"Zheng","year":"2022","journal-title":"arXiv preprint arXiv:2205.09489v2"},{"key":"2023122118393692600_bib72","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1145\/3340531.3411954","article-title":"S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Zhou","year":"2020"}],"container-title":["Transactions of the Association for Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00619\/2200648\/tacl_a_00619.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00619\/2200648\/tacl_a_00619.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T18:40:05Z","timestamp":1703184005000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/tacl\/article\/doi\/10.1162\/tacl_a_00619\/118712\/Pre-train-Prompt-and-Recommendation-A"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":73,"URL":"https:\/\/doi.org\/10.1162\/tacl_a_00619","relation":{},"ISSN":["2307-387X"],"issn-type":[{"value":"2307-387X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023]]},"published":{"date-parts":[[2023]]}}}