Fetching the paper…
Reading the bibliography…
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions.
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.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2023 · 1910
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web . 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Multi-task feature learning for knowledge graph enhanced recommendation. In The world wide web conference . 2000–2010
Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019b · 2010
Earlier work this paper cites.
On the Sentence Embeddings from Pre-trained Language Models
Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020b · 2011
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
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.
Image-based Recommendations on Styles and Substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
Earlier work this paper cites.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Bishan Yang, Wen tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
Earlier work this paper cites.
Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th international conference on data mining (ICDM) . IEEE, 191–200
Ruining He and Julian McAuley. 2016a · 2016
Earlier work this paper cites.
Discrete-state variational autoencoders for joint discovery and factorization of relations
Diego Marcheggiani and Ivan Titov. 2016 · 2016
Earlier work this paper cites.
Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . 353–362
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016 · 2016
Earlier work this paper cites.
Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
Earlier work this paper cites.
Personalizing session-based recommendations with hierarchical recurrent neural networks. In proceedings of the Eleventh ACM Conference on Recommender Systems . 130–137
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017 · 2017
Earlier work this paper cites.
Recurrent recommender networks. In Proceedings of the tenth ACM international conference on web search and data mining . 495–503
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017 · 2017
Earlier work this paper cites.
Learning heterogeneous knowledge base embeddings for explainable recommendation
Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018 · 2018
Cited alongside, same era.
Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Cited alongside, same era.
DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 world wide web conference . 1835–1844
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018 · 2018
Cited alongside, same era.
A Simple Convolutional Generative Network for Next Item Recommendation
LoRA: Low-Rank Adaptation of Large Language Models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Later among the works it cites.
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Later among the works it cites.
One Person, One Model—Learning Compound Router for Sequential Recommendation. In 2022 IEEE International Conference on Data Mining (ICDM) . 289–298
Zhiding Liu, Mingyue Cheng, Zhi Li, Qi Liu, and Enhong Chen. 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. 2018 · 2018
Cited alongside, same era.
Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference . 151–161
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019 · 2019
Cited alongside, same era.
Jointly learning explainable rules for recommendation with knowledge graph. In The world wide web conference . 1210–1221
Weizhi Ma, Min Zhang, Yue Cao, Woojeong Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, and Xiang Ren. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Sequential recommender systems: challenges, progress and prospects
Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z Sheng, and Mehmet Orgun. 2019a · 2019
Cited alongside, same era.
Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 346–353
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019 · 2019
Cited alongside, same era.
Relational collaborative filtering: Modeling multiple item relations for recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval . 125–134
Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. 2019 · 2019
Cited alongside, same era.
Cities: Contextual inference of tail-item embeddings for sequential recommendation. In 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 202–211
Seongwon Jang, Hoyeop Lee, Hyunsouk Cho, and Sehee Chung. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
A systematic review and research perspective on recommender systems
Deepjyoti Roy and Mala Dutta. 2022 · 2022
Later among the works it cites.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
Later among the works it cites.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys ’23) . ACM
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
Later among the works it cites.
Recommender Systems in the Era of Large Language Models (LLMs)
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, and Qing Li. 2023 · 2023
Later among the works it cites.
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
Later among the works it cites.
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2023 · 2023
Later among the works it cites.
Evaluating Open-Domain Question Answering in the Era of Large Language Models
Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rafiei. 2023 · 2023
Later among the works it cites.
Is chatgpt a good recommender? a preliminary study
Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023b · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Benchmarking large language models for news summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B Hashimoto. 2023a · 2023
Later among the works it cites.