[1]SHI Lei,ZHAO Yuqiu,YUAN Ruiping,et al.A survey of generative recommender systems[J].CAAI Transactions on Intelligent Systems,2026,21(1):19-40.[doi:10.11992/tis.202505006]
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A survey of generative recommender systems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 19-40 Column: 综述 Public date: 2026-03-05
Title:
A survey of generative recommender systems
Author(s):
SHI Lei1;  ZHAO Yuqiu1;  YUAN Ruiping2;  ZHONG Yan3;  LIU Yanchao1
1. State Key Laboratory of Media Integration and Communication, Communication University of China, Beijing 100024, China;
2. School of Computer and Artificial Intelligence, Beijing Wuzi University, Beijing 101149, China;
3. School of Mathematical Sciences, Peking University, Beijing 100871, China
Keywords:
recommender system; generative model; large language model; feature tokenization; representation learning; model architecture; collaborative information; evaluation method
CLC:
TP301
DOI:
10.11992/tis.202505006
Abstract:
With the rapid growth of social media content scale, traditional collaborative filtering recommender systems increasingly exhibit limitations in data sparsity and cold start problems. In recent years, the powerful data feature analysis and content generation capabilities of generative models have brought new development opportunities for recommender systems. This paper systematically reviews the technical frameworks and research progress in generative recommender systems, focusing on five key aspects: feature tokenization methods, core model architectural designs, mainstream evaluation protocols and typical application scenarios. Through comparative analysis and literature review, we demonstrate that generative recommender systems significantly outperform conventional approaches in recommendation accuracy, personality, and scenario adaptability. The study further identifies critical challenges including computational overhead, privacy risks, and standardization of evaluation metrics. Practical solutions and future research directions are proposed to address these challenges, breaking the cognitive bottleneck of generative recommender systems.
References:
[1] ZHAO Zihuai, FAN Wenqi, LI Jiatong, et al. Recommender systems in the era of large language models (LLMs)[J]. IEEE transactions on knowledge & data engineering, 2024(1): 1-20.
[2] LI Lei, ZHANG Yongfeng, LIU Dugang, et al. Large language models for generative recommendation: A survey and visionary discussions[EB/OL]. (2024-05-23)[2025-04-20]. http://arxiv.org/abs/2309.01157.
[3] LIN Jianghao, DAI Xinyi, XI Yunjia, et al. How can recommender systems benefit from large language models: a survey[J]. ACM transactions on information systems, 2025, 43(2): 1-47.
[4] VATS A, JAIN V, RAJA R, et al. Exploring the impact of large language models on recommender systems: An extensive review[EB/OL]. (2024-05-19)[2025-04-20]. http://arxiv.org/abs/2402.18590.
[5] WU Likang, ZHENG Zhi, QIU Zhaopeng, et al. A survey on large language models for recommendation[J]. World wide web, 2024, 27(5): 60.
[6] WANG Qi, LI Jindong, WANG Shiqi, et al. Towards next-generation llm-based recommender systems: A survey and beyond[EB/OL]. (2024-10-10)[2025-04-20]. http://arxiv.org/abs/2410.19744.
[7] 卡祖铭, 赵鹏, 张波, 等. 面向大语言模型的推荐系统综述[J]. 计算机科学, 2024, 51(S2): 11-21. KA Zuming, ZHAO Peng, ZHANG Bo, et al. Survey of recommender systems for large language models[J]. Computer science, 2024, 51(S2): 11-21.
[8] WANG Wenjie, LIN Xinyu, FENG Fuli, et al. Generative recommendation: Towards next-generation recommender paradigm[EB/OL]. (2024-02-25)[2025-04-20]. http://arxiv.org/abs/2304.03516.
[9] DELDJOO Y, HE Zhankui, MCAULEY J, et al. A review of modern recommender systems using generative models (gen-recsys)[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Barcelona: ACM, 2024: 6448-6458.
[10] HUANG Chengkai, YU Tong, XIE Kaige, et al. Foundation models for recommender systems: A survey and new perspectives[EB/OL]. (2024-02-17)[2025-04-20]. http://arxiv.org/abs/2402.11143.
[11] 吴国栋, 秦辉, 胡全兴, 等. 大语言模型及其个性化推荐研究[J]. 智能系统学报, 2024, 19(6): 1351-1365. WU Guodong, QIN Hui, HU Quanxing, et al. Research on large language models and personalized recommendation[J]. CAAI transactions on intelligent systems, 2024, 19(6): 1351-1365.
[12] SUN Fei, LIU Jun, WU Jian, et al. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer[C]//Proceedings of the 28th ACM International Conference on Information and KnowLedge Management. Beijing: ACM, 2019: 1441-1450.
[13] KANG W C, MCAULEY J. Self-attentive sequential recommendation [C]//2018 IEEE International Conference on Data Mining. Singapore: IEEE, 2018: 197-206.
[14] HUA Wenyue, XU Shuyuan, GE Yingqiang, et al. How to index item ids for recommendation foundation models[C]//Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region. Beijing: ACM, 2023: 195-204.
[15] HOU Yupeng, NI Jianmo, HE Zhankui, et al. ActionPiece: Contextually tokenizing action sequences for generative recommendation[EB/OL]. (2025-02-19)[2025-04-20]. http://arxiv.org/abs/2502.13581.
[16] LIN Xinyu, WANG Wenjie, LI Yongqi, et al. Bridging items and language: A transition paradigm for large language model-based recommendation[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Barcelona: ACM, 2024: 1816-1826.
[17] QIU Junyan, WANG Haitao, HONG Zhaolin, et al. ControlRec: Bridging the semantic gap between language model and personalized recommendation[EB/OL]. (2023-11-28)[2025-04-20]. http://arxiv.org/abs/2311.16441.
[18] JIN Bowen, ZENG Hansi, WANG Guoyin, et al. Language models as semantic indexers[C]//Proceedings of the 41st International Conference on Machine Learning. Vienna: ACM, 2024: 22244-22259.
[19] ZHAI Jiaqi, LIAO L, LIU Xing, et al. Actions speak louder than words: trillion-parameter sequential transducers forgenerative recommendations[C]//Proceedings of the 41st International Conference on Machine Learning. Vienna: ACM, 2024: 58484-58509.
[20] HOU Yupeng, HE Zhankui, MCAULEY J, et al. Learning vector-quantized item representation for transferable sequential recommenders[C]//Proceedings of the ACM Web Conference 2023. Austin: ACM, 2023: 1162-1171.
[21] QU Haohao, FAN Wenqi, ZHAO Zihuai, et al. Tokenrec: learning to tokenize id for llm-based generative recommendation[EB/OL]. (2024-08-18)[2025-04-20]. http://arxiv.org/abs/2406.10450.
[22] LIU Qijiong, XIAO Jiaren, FAN Llu, et al. Learning category trees for ID-vased recommendation: Exploring the power of differentiable vector quantization[C]//Proceedings of the ACM Web Conference 2024. Singapore: ACM, 2024: 3521-3532.
[23] RAJPUT S, MEHTA N, SINGH A, et al. Recommender systems with generative retrieval[J]. Advances in neural information processing systems, 2023, 36: 10299-10315.
[24] ZHU Jieming, JIN Mengqun, LIU Qijiong, et al. CoST: contrastive quantization based semantic tokenization for generative recommendation[C]//Proceedings of the 18th ACM Conference on Recommender Systems. Bari: ACM, 2024: 969-974.
[25] ZHENG Bowen, HOU Yupeng, LU Hongyu, et al. Adapting large language models by integrating collaborative semantics for recommendation[C]//2024 IEEE 40th International Conference on Data Engineering. Utrecht: IEEE, 2024: 1435-1448.
[26] WANG Wenjie, BAO Honghui, LIN Xinyu, et al. Learnable item tokenization for generative recommendation[C]//Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. Boise: ACM, 2024: 2400-2409.
[27] LIN Xinyu, SHI Haihan, WANG Wenjie, et al. Order-agnostic identifier for large language model-based generative recommendation[C]//Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. Padua: ACM, 2025: 1923-1933.
[28] LIU Zihan, HOU Yupeng, MCAULEY J. Multi-behavior generative recommendation[C]//Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. Boise: ACM, 2024: 1575-1585.
[29] SINGH A, VU T, MEHTA N, et al. Better generalization with semantic ids: A case study in ranking for recommendations[C]//Proceedings of the 18th ACM Conference on Recommender Systems. Bari: ACM, 2024: 1039-1044.
[30] XI Yunjia, LIU Weiwen, LIN Jianghao, et al. Towards open-world recommendation with knowledge augmentation from large language models[C]//Proceedings of the 18th ACM Conference on Recommender Systems. Bari: ACM, 2024: 12-22.
[31] GENG Shijie, LIU Shuchang, FU Zuohui, et al. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)[C]//Proceedings of the 16th ACM Conference on Recommender Systems. Seattle: ACM, 2022: 299-315.
[32] LI Yangning, MA Shirong, WANG Xiaobin, et al. EcomGPT: instruction-tuning large language models with chain-of-task tasks for E-commerce[C]//Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence. Vancouver: ACM, 2024: 18582-18590.
[33] LI Jinming, ZHANG Wentao, WANG Tian, et al. GPT4Rec: A generative framework for personalized recommendation and user interests interpretation[EB/OL]. (2023-04-19)[2025-04-20]. http://arxiv.org/abs/2304.03879.
[34] MYSORE S, MCCALLUM A, ZAMANI H. Large language model augmented narrative driven recommendations[C]//Proceedings of the 17th ACM Conference on Recommender Systems. Singapore: ACM, 2023: 777-783.
[35] TAN Juntao, XU Shuyuan, HUA Wenyue, et al. Idgenrec: Llm-recsys alignment with textual id learning[C]//Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Washington DC: ACM, 2024: 355-364.
[36] LI Pan, WANG Yuyan, CHI E H, et al. Prompt tuning large language models on personalized aspect extraction for recommendations[EB/OL]. (2023-06-02)[2025-04-20]. http://arxiv.org/abs/2306.01475.
[37] XU Da, RUAN Chuanwei, KORPEOGLU E, et al. Product knowledge graph embedding for e-commerce[C]//Proceedings of the 13th International Conference on Web Search and Data Mining. Houston: ACM, 2020: 672-680.
[38] FAN Ziwei, LIU Zhiwei, HEINECKE S, et al. Zero-shot item-based recommendation via multi-task product knowledge graph pre-training[C]//Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Birmingham: ACM, 2023: 483-493.
[39] SEDHAIN S, MENON A K, SANNER S, et al. Autorec: Autoencoders meet collaborative filtering[C]//Proceedings of the 24th International Conference on World Wide Web. Florence: ACM, 2015: 111-112.
[40] YI Baolin, SHEN Xiaoxuan, ZHANG Zhaoli, et al. Expanded autoencoder recommendation framework and its application in movie recommendation[C]//2016 10th International Conference on Software, Knowledge, Information Management & Applications. Chengdu: IEEE, 2016: 298-303.
[41] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th International Conference on Machine learning. Helsinki: ACM, 2008: 1096-1103.
[42] WU Yao, DUBOIS C, ZHENG A X, et al. Collaborative denoising auto-encoders for top-n recommender systems[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. San Francisco: ACM, 2016: 153-162.
[43] WANG Hao, SHI Xingjian, YEUNG D Y. Relational stacked denoising autoencoder for tag recommendation[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Austin: ACM, 2015: 3052-3058.
[44] WANG Hao, WANG Naiyan, YEUNG D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney: ACM, 2015: 1235-1244.
[45] KINGMA D P, WELLING M. Auto-encoding variational bayes[EB/OL]. (2013-12-20)[2025-04-20]. http://arxiv.org/abs/1312.6114.
[46] LIANG Dawen, KRISHNAN R G, HOFFMAN M D, et al. Variational autoencoders for collaborative filtering[C]//Proceedings of the 2018 World Wide Web Conference. Lyon: ACM, 2018: 689-698.
[47] WU Ga, BOUADJENEK M R, SANNER S. One-class collaborative filtering with the queryable variational autoencoder[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Paris: ACM, 2019: 921-924.
[48] VO T V, SOH H. Generation meets recommendation: proposing novel items for groups of users[C]//Proceedings of the 12th ACM Conference on Recommender Systems. Vancouver: ACM, 2018: 145-153.
[49] XIE Zhe, LIU Chengxuan, ZHANG Yichi, et al. Adversarial and contrastive variational autoencoder for sequential recommendation[C]//Proceedings of the Web Conference 2021. Ljubljana: ACM, 2021: 449-459.
[50] YI Jing, CHEN Zhenzhong. Multi-modal variational graph auto-encoder for recommendation systems[J]. IEEE transactions on multimedia, 2021, 24: 1067-1079.
[51] ZHANG Yi, ZHANG Yiwen, YAN D, et al. Revisiting graph-based recommender systems from the perspective of variational auto-encoder[J]. ACM transactions on information systems., 2023, 41(3): 1-28.
[52] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[C]. Proceedings of the 11th ACM Conference on Recommender Systems. Como: ACM, 2017: 130–137.
[53] QUADRANA M, KARATZOGLOU A, HIDASI B, et al. Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the 11th ACM Conference on Recommender Systems. Como: ACM, 2017: 130-137.
[54] YU Feng, LIU Qiang, WU Shu, et al. A dynamic recurrent model for next basket recommendation[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. Pisa: ACM, 2016: 729-732.
[55] WANG Chen, YUAN Mengting, YANG Yang, et al. Revisiting long-and short-term preference learning for next POI recommendation with hierarchical LSTM[J]. IEEE transactions on mobile computing, 2024.
[56] ZHU Yu, LI Hao, LIAO Yikang, et al. What to do next: modeling user behaviors by time-LSTM[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne: ACM, 2017: 3602-3608.
[57] LIU Chengkai, LIN Jianghao, WANG Jianling, et al. Mamba4rec: Towards efficient sequential recommendation with selective state space models[EB/OL]. (2024-06-29)[2025-04-20]. http://arxiv.org/abs/2403.03900.
[58] YANG Jiyuan, LI Yuanzi, ZHAO Jingyu, et al. Uncovering selective state space model’s capabilities in lifelong sequential recommendation[EB/OL]. (2024-05-25)[2025-04-20]. http://arxiv.org/abs/2403.16371.
[59] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[EB/OL]. (2017-06-12)[2025-04-20]. http://arxiv.org/abs/1706.03762.
[60] WU Liwei, LI Shuqing, HSIEH C J, et al. SSE-PT: Sequential recommendation via personalized transformer[C]//Proceedings of the 14th ACM Conference on Recommender Systems. Virtual Event: ACM, 2020: 328-337.
[61] De SOUZA P M G, RABHI S, LEE J M, et al. Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation[C]//Proceedings of the 15th ACM Conference on Recommender Systems. Amsterdam: ACM, 2021: 143-153.
[62] YUAN Enming, GUO Wei, HE Zhicheng, et al. Multi-behavior sequential transformer recommender[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid: ACM, 2022: 1642-1652.
[63] HUANG Xiaowen, QIAN Shengsheng, FANG Quan, et al. Csan: Contextual self-attention network for user sequential recommendation[C]//Proceedings of the 26th ACM International Conference on Multimedia. Seoul: ACM, 2018: 447-455.
[64] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27.
[65] WANG Jun, YU Lantao, ZHANG Weinian, et al. Irgan: a minimax game for unifying generative and discriminative information retrieval models[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Shinjuku: ACM, 2017: 515-524.
[66] CHAE D K, KANG J S, KIM S W, et al. CFGAN: a generic collaborative filtering framework based on generative adversarial networks[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino: ACM, 2018: 137-146.
[67] WU Qiong, LIU Yong, MIAO Chunyan, et al. PD-GAN: adversarial learning for personalized diversity-promoting recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao: ACM, 2019: 3870-3876.
[68] ZHOU Fan, YIN Ruiyang, ZHANG Kunpeng, et al. Adversarial point-of-interest recommendation[C]//The World Wide Web Conference. San Francisco: ACM, 2019: 3462-34618.
[69] FAN Wenqi, DERR T, MA Yao, et al. Deep adversarial social recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao: ACM, 2019: 1351-1357.
[70] PERERA D, ZIMMERMANN R. CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users[C]//The World Wide Web Conference. San Francisco: ACM, 2019: 3144-3150.
[71] YU Xianwen, ZHANG Xiaoning, CAO Yang, et al. VAEGAN: a collaborative filtering framework based on adversarial variational autoencoders[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao: ACM, 2019: 4206-4212.
[72] SOHL-DICKSTEIN J, WEISS E A, MAHESWARANATHAN N, et al. Deep unsupervised learning using nonequilibrium thermodynamics[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning. Lille: ACM, 2015: 2256-2265.
[73] WANG Wenjie, XU Yiyan, FENG Fuli, et al. Diffusion recommender model[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Taipei: ACM, 2023: 832-841.
[74] HOU Yu, PARK J D, SHIN W Y. Collaborative filtering based on diffusion models: Unveiling the potential of high-order connectivity[C]//Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Washington DC: ACM, 2024: 1360-1369.
[75] ZHU Yunqin, WANG Chao, ZHANG Qi, et al. Graph signal diffusion model for collaborative filtering[C]//Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Washington DC: ACM, 2024: 1380-1390.
[76] YANG Zhengyi, WU Jiancan, WANG Zhicai, et al. Generate what you prefer: reshaping sequential recommendation via guided diffusion[C]//Proceedings of the 37th International Conference on Neural Information Processing Systems. New Orleans: ACM, 2023: 24247-24261.
[77] LI Zihao, SUN Aixin, LI Chenliang. Diffurec: A diffusion model for sequential recommendation[J]. ACM transactions on information systems, 2023, 42(3): 1-28.
[78] YANG Hao, YUAN Jianxin, YANG Shuai, et al. A new creative generation pipeline for click-through rate with stable diffusion model[C]//Companion Proceedings of the ACM Web Conference 2024. Singapore: ACM, 2024: 180-189.
[79] CZAPP ? T, JANI M, DOMI?N B, et al. Dynamic product image generation and recommendation at scale for personalized ecommerce[C]//Proceedings of the 18th ACM Conference on Recommender Systems. Bari: ACM, 2024: 768-770.
[80] WU Chuhan, WU Fangzhao, QI Tao, et al. PTUM: Pre-training user model from unlabeled user behaviors via self-supervision[C]//Findings of the Association for Computational Linguistics: EMNLP 2020. Online: ACL, 2020: 1939-1944.
[81] NGO H, NGUYEN D Q. RecGPT: Generative pre-training for text-based recommendation[C]//Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics. Bangkok: ACL, 2024: 302-313.
[82] CUI Zeyu, MA Jianxin, ZHOU Chang, et al. M6-rec: Generative pretrained language models are open-ended recommender systems[EB/OL]. (2022-05-19)[2025-04-20]. http://arxiv.org/abs/2205.08084.
[83] SANNER S, BALOG K, RADLINSKi F, et al. Large language models are competitive near cold-start recommenders for language-and item-based preferences[C]//Proceedings of the 17th ACM Conference on Recommender Systems. Singapore: ACM, 2023: 890-896.
[84] WANG Xiaolei, TANG Xinyu, ZHAO Xin, et al. Rethinking the evaluation for conversational recommendation in the era of large language models[C]//Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapore: ACL 2023: 10052-10065.
[85] WANG Lei, LIM E P. The whole is better than the sum: Using aggregated demonstrations in in-context learning for sequential recommendation[C]//Findings of the Association for Computational Linguistics: NAACL 2024. Mexico City: ACL, 2024: 876-895.
[86] LIU Dairui, YANG Boming, DU Honghui, et al. RecPrompt: a self-tuning prompting framework for news recommendation using large language models[C]//Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. Boise: ACM, 2024: 3902-3906.
[87] WANG Yuling, TIAN Changxin, HU Binbin, et al. Can small language models be good reasoners for sequential recommendation?[C]//Proceedings of the ACM Web Conference 2024. Singapore: ACM, 2024: 3876-3887.
[88] TSAI A, KRAFT A, JIN Long, et al. Leveraging LLM reasoning enhances personalized recommender systems[C]//Findings of the Association for Computational Linguistics ACL 2024. Bangkok: ACL, 2024: 13176-13188.
[89] SHEN Tianshu, LI Jiaru, BOUADJENEK M R, et al. Towards understanding and mitigating unintended biases in language model-driven conversational recommendation[J]. Information processing and management, 2023, 60(1): 103139.
[90] ZHANG Yang, FENG Fuli, ZHANG Jizhi, et al. CoLLM: Integrating collaborative embeddings into large language models for recommendation[J]. IEEE transactions on knowledge and data engineering, 2025(1): 1-12.
[91] GENG Shijie, TAN Juntao, LIU Shuchang, et al. VIP5: Towards multimodal foundation models for recommendation[C]//Findings of the Association for Computational Linguistics: EMNLP 2023. Singapore: ACL, 2023: 9606-9620.
[92] JIANG Junzhe, QU Shang, CHENG Mingyue, et al. Reformulating sequential recommendation: learning dynamic user interest with content-enriched language modeling[C]//International Conference on Database Systems for Advanced Applications. Gifu: ACM, 2024: 353-362.
[93] ZHANG Zizhuo, WANG Bang. Prompt learning for news recommendation[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Taipei: ACM, 2023: 227-237.
[94] WANG Xiaolei, ZHOU Kun, WEN Jirong, et al. Towards unified conversational recommender systems via knowledge-enhanced prompt learning[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Washington DC: ACM, 2022: 1929-1937.
[95] WEI Wei, TANG Jiabin, XIA Lianghao, et al. Promptmm: Multi-modal knowledge distillation for recommendation with prompt-tuning[C]//Proceedings of the ACM Web Conference 2024. Singapore: ACM, 2024: 3217-3228.
[96] LUO Sichun, HE Bowei, ZHAO Haohan, et al. Recranker: Instruction tuning large language model as ranker for top-k recommendation[EB/OL]. (2023-12-26)[2025-12-23]. https://arxiv.org/abs/2312.16018.
[97] LIU Huafeng, JING Liping, WEN Jingxuan, et al. Interpretable deep generative recommendation models[J]. Journal of machine learning research, 2021, 22(202): 1-54.
[98] LIU Huafeng, WEN Jingxuan, JING Liping, et al. Deep generative ranking for personalized recommendation[C]//Proceedings of the 13th ACM Conference on Recommender Systems. Copenhagen: ACM, 2019: 34-42.
[99] LIU Shuchang, CAI Qingpeng, HE Zhankui, et al. Generative flow network for listwise recommendation[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Long Beach: ACM, 2023: 1524-1534.
[100] YELP I.Yelp Dataset[EB/OL]. (2014)[2025-12-23]. https://www.yelp.com/dataset.
[101] WU Fangzhao, QIAO Ying, CHEN J H, et al. Mind: a large-scale dataset for news recommendation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: ACL, 2020: 3597-3606.
[102] YUAN Guanghu, YUAN Fajie, LI Yudong, et al. Tenrec: a large-scale multipurpose benchmark dataset for recommender systems[C]//Proceedings of the 36th International Conference on Neural Information Processing Systems. New Orleans: ACM, 2022: 11480-11493.
[103] HARPER F M, KONSTAN J A. The movielens datasets: History and context[J]. ACM transactions on interactive intelligent systems, 2015, 5(4): 1-19.
[104] NI Jianmo, LI Jiacheng, MCAULEY J. Justifying recommendations using distantly-labeled reviews and fine-grained aspects[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong: ACL, 2019: 188-197.
[105] WAN Mengting, MCAULEY J. Item recommendation on monotonic behavior chains[C]//Proceedings of the 12th ACM Conference on Recommender Systems. Vancouver: ACM, 2018: 86-94.
[106] WU Yu, WU Wei, XING Chen, et al. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based Chatbots[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver: ACL, 2017: 496-505.
[107] CHENG Yu, PAN Yunzhu, ZHANG Jiaqi, et al. An image dataset for benchmarking recommender systems with raw pixels[C]//Proceedings of the 2024 SIAM International Conference on Data Mining (SDM). Texas: SIAM, 2024: 418-426.
[108] NI Yongxin, CHENG Yu, Liu Xiangyan, et al. A content-driven micro-video recommendation dataset at scale[EB/OL]. (2023-09-27)[2025-04-20]. http://arxiv.org/abs/2309.15379.
[109] SUN Zhongxiang, SI Zihua, ZANG Xiaoxue, et al. KuaiSar: a unified search and recommendation dataset[C]//Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Birmingham: ACM, 2023: 5407-5411.
[110] GAO Chongming, LI Shijun, LEI Wenqiang, et al. KuaiRec: a fully-observed dataset and insights for evaluating recommender systems[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Atlanta: ACM, 2022: 540-550.
[111] JIN Wei, MAO Haitao, LI Zheng, et al. Amazon-M2: a multilingual multi-locale shopping session dataset for recommendation and text generation[C]//Proceedings of the 37th International Conference on Neural Information Processing Systems. New Orleans: ACM, 2023: 8006-8026.
[112] LI R, EBRAHIMI KAHOU S, SCHULZ H, et al. Towards deep conversational recommendations[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montreal: ACM, 2018: 9748-9758.
[113] LIU Yuanxing, ZHANG Weinan, DONG Baohua, et al. U-need: A fine-grained dataset for user needs-centric e-commerce conversational recommendation[C]//Proceedings of the 46th international ACM SIGIR Conference on Research and Development in Information Retrieval. Taipei: ACM, 2023: 2723-2732.
[114] HAYATI S A, KANG D, ZHU Q, et al. INSPIRED: Toward sociable recommendation dialog systems[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Online: ACL, 2020: 8142-8152.
[115] ZHANG Jiaqi, CHENG Yu, NI Yongxin, et al. NineRec: A benchmark dataset suite for evaluating transferable recommendation[J]. IEEE transactions on pattern analysis and machine intelligence, 2024(1): 1-12.
[116] DING Yijie, HOU Yupeng, LI Jiacheng, et al. Inductive generative recommendation via retrieval-based speculation[EB/OL]. (2024-11-03)[2025-04-20]. http://arxiv.org/abs/2410.02939.
[117] XU Yuanbo, WANG En, YANG Yongjian, et al. GS-RS: A generative approach for alleviating cold start and filter bubbles in recommender systems[J]. IEEE transactions on knowledge and data engineering, 2023, 36(2): 668-681.
[118] BAI Haoyue, HOU Min, WU Le, et al. Gorec: a generative cold-start recommendation framework[C]//Proceedings of the 31st ACM International Conference on Multimedia. Ottawa: ACM, 2023: 1004-1012.
[119] HUANG Feiran, BEI Yuanchen, YANG Zhenghang, et al. Large language model simulator for cold-start recommendation[C]//Proceedings of the 18th ACM International Conference on Web Search and Data Mining. Hannover: ACM, 2025: 261-270.
[120] KUSANO G. Data Augmentation using reverse prompt for cost-Efficient cold-start recommendation[C]//Proceedings of the 18th ACM Conference on Recommender Systems. Bari: ACM, 2024: 861-865.
[121] WU Xuansheng, ZHOU Huachi, SHI Yucheng, et al. Could small language models serve as recommenders? Towards data-centric cold-start recommendation[C]//Proceedings of the ACM Web Conference 2024. Singapore: ACM, 2024: 3566-3575.
[122] JIANG Yuezihan, CHEN Gaode, ZHANG Wenhan, et al. Prompt tuning for item cold-start recommendation[C]//Proceedings of the 18th ACM Conference on Recommender Systems. Bari: ACM, 2024: 411-421.
[123] FANG Yi, WANG Wenjie, ZHANG Yang, et al. Large language models for recommendation with deliberative user preference alignment[EB/OL]. (2025-02-17)[2025-04-20]. http://arxiv.org/abs/2502.02061.
[124] LIAO Jiayi, HE Xiangnan, XIE Ruobing, et al. RosePO: Aligning LLM-based recommenders with human values[EB/OL]. (2024-10-16)[2025-04-20]. http://arxiv.org/abs/2410.12519.
[125] SHAO Minglai, HUANG Hua, PENG Qiyao, et al. ULMRec: user-centric large language model for sequential recommendation[EB/OL]. (2024-12-07)[2025-04-20]. http://arxiv.org/abs/2412.05543.
[126] DENG Jiaxin, WANG Shiyao, CAI Kuo, et al. OneRec: Unifying retrieve and rank with generative recommender and iterative preference alignment[EB/OL]. (2025-02-26)[2025-04-20]. http://arxiv.org/abs/2502.18965.
[127] GAO Chongming, CHEN Ruijun, YUAN Shuai, et al. SPRec: Self-play to debias LLM-based recommendation[C]//Proceedings of the ACM on Web Conference 2025. Sydney: ACM, 2025: 5075-5084.
[128] BIN Xingyan, CUI Jianfei, YAN Wujie, et al. Real-time indexing for large-scale recommendation by streaming vector quantization retriever[EB/OL]. (2025-01-15)[2025-04-20]. http://arxiv.org/abs/2501.08695.
[129] ZHOU Yujia, YAO Jing, DOU Zhicheng, et al. ROGER: Ranking-oriented generative retrieval[J]. ACM Transactions on information systems, 2024, 42(6): 1-25.
[130] YANG Yuhao, JI Zhi, LI Zhaopeng, et al. Sparse meets dense: Unified generative recommendations with cascaded sparse-dense representations[EB/OL]. (2025-03-04)[2025-04-20]. http://arxiv.org/abs/2503.02453.
[131] PENHA G, VARDASBI A, PALUMBO E, et al. Bridging search and recommendation in generative retrieval: Does one task help the other?[C]//Proceedings of the 18th ACM Conference on Recommender Systems. Bari: ACM, 2024: 340-349.
[132] CHEN Sirui, HAN Shen, CHEN Jiawei, et al. Rankformer: A graph transformer for recommendation based on ranking objective[C]//Proceedings of the ACM on Web Conference 2025. Sydney: ACM, 2025: 3037-3048.
[133] WANG Yilin, HU Minghao, HUANG Zhen, et al. KC-GenRe: a knowledge-constrained generative re-ranking method based on large language models for knowledge graph completion[C]//Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Torino: ACL, 2024: 9668-9680.
[134] YAN Bencheng, LIU Shilei, ZENG Zhiyuan, et al. Unlocking scaling law in industrial recommendation systems with a three-step paradigm based large user model[EB/OL]. (2025-02-12)[2025-04-20]. http://arxiv.org/abs/2502.08309.
[135] JIA J, WANG Y, LI Y, et al. LEARN: knowledge adaptation from large language model to recommendation for practical industrial application[C]//Proceedings of the 39th AAAI Conference on Artificial Intelligence. Philadelphia: ACM, 2025: 11861-11869.
[136] ZHAO Qian, QIAN Hao, LIU Ziqi, et al. Breaking the barrier: utilizing large language models for industrial recommendation systems through an inferential knowledge graph[C]//Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. Boise: ACM, 2024: 5086-5093.
[137] HU Jun, XIA Wenwen, ZHANG Xiaolu, et al. Enhancing sequential recommendation via llm-based semantic embedding learning[C]//Companion Proceedings of the ACM Web Conference 2024. Singapore: ACM, 2024: 103-111.
[138] LIU Enze, ZHENG Bowen, LING Cheng, et al. End-to-end learnable item tokenization for generative recommendation[EB/OL]. (2025-03-12)[2025-04-20]. http://arxiv.org/abs/2409.05546.
[139] 孟岱. 工业大模型的落地难题[J]. 中国工业和信息化, 2024(4): 26-32. MENG Dai. The landing challenge of industrial large models[J]. China industry and information technology, 2024(4): 26-32.
[140] LIU Fan, CHENG Zhiyong, CHEN Huilin, et al. Privacy-preserving synthetic data generation for recommendation systems[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid: ACM, 2022: 1379-1389.
[141] 黄河燕, 李思霖, 兰天伟, 等. 大语言模型安全性: 分类、评估、归因、缓解、展望[J]. 智能系统学报, 2025, 20(1): 2-32. HUANG Heyan, LI Silin, LAN Tianwei, et al. A survey on the safety of large language model: classification, evaluation, attribution, mitigation and prospect[J]. CAAI transactions on intelligent systems, 2025, 20(1): 2-32.
[142] GUO Daya, YANG Dejian, ZHANG Haowei, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning[EB/OL]. (2025-01-22)[2025-04-20]. http://arxiv.org/abs/2501.12948.
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