Fetching the paper…
Reading the bibliography…
Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Large Language Models are Learnable Planners for Long-Term Recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1893–1903
Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, and Fuli Feng. 2024 · 1903
Earlier work this paper cites.
Accurate sum and dot product
Takeshi Ogita, Siegfried M Rump, and Shin’ichi Oishi. 2005 · 1988
Earlier work this paper cites.
Approximation theory of the MLP model in neural networks
Allan Pinkus. 1999 · 1999
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Romero Adriana, Ballas Nicolas, K Samira Ebrahimi, Chassang Antoine, Gatta Carlo, and Bengio Yoshua. 2015 · 2015
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Learning from multiple teacher networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 1285–1294
Shan You, Chang Xu, Chao Xu, and Dacheng Tao. 2017 · 2017
Earlier work this paper cites.
Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining . 108–116
Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
On the efficacy of knowledge distillation. In Proceedings of the IEEE/CVF international conference on computer vision . 4794–4802
Jang Hyun Cho and Bharath Hariharan. 2019 · 2019
Earlier work this paper cites.
Collaborative distillation for top-N recommendation. In 2019 IEEE International Conference on Data Mining (ICDM) . IEEE, 369–378
Jae-woong Lee, Minjin Choi, Jongwuk Lee, and Hyunjung Shim. 2019 · 2019
Earlier work this paper cites.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management . 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Earlier work this paper cites.
Sequential recommender systems: challenges, progress and prospects
Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z Sheng, and Mehmet Orgun. 2019 · 2019
Earlier work this paper cites.
Agree to disagree: Adaptive ensemble knowledge distillation in gradient space
Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, and Changshui Zhang. 2020 · 2020
Earlier work this paper cites.
Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations
Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo. 2020 · 2020
Earlier work this paper cites.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
DE-RRD: A knowledge distillation framework for recommender system. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 605–614
SeongKu Kang, Junyoung Hwang, Wonbin Kweon, and Hwanjo Yu. 2020 · 2020
Earlier work this paper cites.
Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining . 322–330
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020 · 2020
Earlier work this paper cites.
Binary cross entropy with deep learning technique for image classification
Usha Ruby and Vamsidhar Yendapalli. 2020 · 2020
Earlier work this paper cites.
Ensembled CTR prediction via knowledge distillation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2941–2958
Jieming Zhu, Jinyang Liu, Weiqi Li, Jincai Lai, Xiuqiang He, Liang Chen, and Zibin Zheng. 2020 · 2020
Earlier work this paper cites.
Sequential recommendation with graph neural networks. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval . 378–387
Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021 · 2021
Earlier work this paper cites.
AutoDebias: Learning to debias for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 21–30
Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang. 2021 · 2021
Earlier work this paper cites.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021 · 2021
Cited alongside, same era.
Topology distillation for recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 829–839
SeongKu Kang, Junyoung Hwang, Wonbin Kweon, and Hwanjo Yu. 2021 · 2021
Cited alongside, same era.
Dual correction strategy for ranking distillation in top-n recommender system. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 3186–3190
Youngjune Lee and Kee-Eung Kim. 2021 · 2021
Cited alongside, same era.
Towards universal sequence representation learning for recommender systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 585–593
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022 · 2022
Cited alongside, same era.
Knowledge distillation from a stronger teacher
Tao Huang, Shan You, Fei Wang, Chen Qian, and Chang Xu. 2022 · 2022
SIGformer: Sign-aware Graph Transformer for Recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1274–1284
Sirui Chen, Jiawei Chen, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, and Can Wang. 2024 · 2024
Closest in time.
Breaking the length barrier: Llm-enhanced CTR prediction in long textual user behaviors. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2311–2315
Binzong Geng, Zhaoxin Huan, Xiaolu Zhang, Yong He, Liang Zhang, Fajie Yuan, Jun Zhou, and Linjian Mo. 2024 · 2024
Closest in time.
Large language models are zero-shot rankers for recommender systems. In European Conference on Information Retrieval . Springer, 364–381
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024 · 2024
Closest in time.
Exact and Efficient Unlearning for Large Language Model-based Recommendation
Zhiyu Hu, Yang Zhang, Minghao Xiao, Wenjie Wang, Fuli Feng, and Xiangnan He. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Revisiting graph based social recommendation: A distillation enhanced social graph network. In Proceedings of the ACM Web Conference 2022 . 2830–2838
Ye Tao, Ying Li, Su Zhang, Zhirong Hou, and Zhonghai Wu. 2022 · 2022
Cited alongside, same era.
Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE) . IEEE, 1259–1273
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022 · 2022
Cited alongside, same era.
Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, and Wei Wu. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
A bi-step grounding paradigm for large language models in recommendation systems
Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Fuli Feng, Xiangnaan He, and Qi Tian. 2023a · 2023
Cited alongside, same era.
Bias and debias in recommender system: A survey and future directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023b · 2023
Cited alongside, same era.
Chat-rec: Towards interactive and explainable llms-augmented recommender system
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023 · 2023
Cited alongside, same era.
Item-side Fairness of Large Language Model-based Recommendation System. In Proceedings of the ACM on Web Conference 2024 . 4717–4726
Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, and Xiangnan He. 2024 · 2024
Closest in time.
Llara: Large language-recommendation assistant. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1785–1795
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, and Xiangnan He. 2024 · 2024
Closest in time.
Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation. In Proceedings of the ACM on Web Conference 2024 . 3497–3508
Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024b · 2024
Closest in time.
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Siyi Lin, Chongming Gao, Jiawei Chen, Sheng Zhou, Binbin Hu, and Can Wang. 2024a · 2024
Closest in time.
D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
Jiarui Qin, Weiwen Liu, Ruiming Tang, Weinan Zhang, and Yong Yu. 2024 · 2024
Closest in time.
Recommender systems with generative retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al · 2024
Closest in time.
Representation learning with large language models for recommendation. In Proceedings of the ACM on Web Conference 2024 . 3464–3475
Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
Closest in time.
Large Language Models Enhanced Collaborative Filtering
Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, and Jun Xu. 2024 · 2024
Closest in time.
IDGenRec: LLM-RecSys Alignment with Textual ID Learning. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 355–364
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, and Yongfeng Zhang. 2024 · 2024
Closest in time.
Distributionally Robust Graph-based Recommendation System. In Proceedings of the ACM on Web Conference 2024 . 3777–3788
Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, and Can Wang. 2024a · 2024
Closest in time.
LLM4DSR: Leveraing Large Language Model for Denoising Sequential Recommendation
Bohao Wang, Feng Liu, Jiawei Chen, Yudi Wu, Xingyu Lou, Jun Wang, Yan Feng, Chun Chen, and Can Wang. 2024c · 2024
Closest in time.
Enhanced Generative Recommendation via Content and Collaboration Integration
Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, et al · 2024
Closest in time.
Can Small Language Models be Good Reasoners for Sequential Recommendation?. In Proceedings of the ACM on Web Conference 2024 . 3876–3887
Yuling Wang, Changxin Tian, Binbin Hu, Yanhua Yu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Liang Pang, and Xiao Wang. 2024f · 2024
Closest in time.
Llmrec: Large language models with graph augmentation for recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining . 806–815
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
Closest in time.
Enhancing Content-based Recommendation via Large Language Model
Wentao Xu, Qianqian Xie, Shuo Yang, Jiangxia Cao, and Shuchao Pang. 2024 · 2024
Closest in time.
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, and Mingchen Cai. 2024 · 2024
Closest in time.
Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
Wenlin Zhang, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, Ruiming Tang, et al · 2024
Closest in time.
Harnessing large language models for text-rich sequential recommendation. In Proceedings of the ACM on Web Conference 2024 . 3207–3216
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, and Hui Xiong. 2024 · 2024
Closest in time.
Collaborative large language model for recommender systems. In Proceedings of the ACM on Web Conference 2024 . 3162–3172
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. 2024 · 2024
Closest in time.
Ukd: Debiasing conversion rate estimation via uncertainty-regularized knowledge distillation. In Proceedings of the ACM Web Conference 2022 . 2078–2087
Zixuan Xu, Penghui Wei, Weimin Zhang, Shaoguo Liu, Liang Wang, and Bo Zheng. 2022 · 2087
Closest in time.