A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

A survey of collaborative filtering techniques

Xiaoyuan Su and Taghi M Khoshgoftaar. 2009 · 2009

Earlier work this paper cites.

Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)

Original

Diederik P. Kingma and Jimmy Ba. 2015 · 2015

Earlier work this paper cites.

Session-based Recommendations with Recurrent Neural Networks. In ICLR

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016

Earlier work this paper cites.

Sequential user-based recurrent neural network recommendations. In Proceedings of the eleventh ACM conference on recommender systems . 152–160

Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017 · 2017

Earlier work this paper cites.

Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998–6008

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017

Earlier work this paper cites.

Multimodal grounding for language processing

Original

Lisa Beinborn, Teresa Botschen, and Iryna Gurevych. 2018 · 2018

Earlier work this paper cites.

MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation

Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong, and Liang Wang. 2020 · 2018

Earlier work this paper cites.

Self-Attentive Sequential Recommendation. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018 . IEEE Computer Society, 197–206

Wang-Cheng Kang and Julian J. McAuley. 2018 · 2018

Earlier work this paper cites.

Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018 , Yike Guo and Faisal Farooq (Eds.). ACM, 596–605

Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, Anxiang Zeng, and Luo Si. 2018 · 2018

Earlier work this paper cites.

Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM . 565–573

Jiaxi Tang and Ke Wang. 2018 · 2018

Earlier work this paper cites.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In 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) . Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019

Earlier work this paper cites.

Decoupled Weight Decay Regularization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net

Ilya Loshchilov and Frank Hutter. 2019 · 2019

Earlier work this paper cites.

Language Models are Unsupervised Multitask Learners

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019

Earlier work this paper cites.

Sequential Recommender Systems: Challenges, Progress and Prospects. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019 , Sarit Kraus (Ed.). ijcai.org, 6332–6338

Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, and Mehmet A. Orgun. 2019 · 2019

Earlier work this paper cites.

CosRec: 2D Convolutional Neural Networks for Sequential Recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019 , Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 2173–2176

An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, and Julian J. McAuley. 2019 · 2019

Earlier work this paper cites.

A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019 , J. Shane Culpepper, Alistair Moffat, Paul N. Bennett, and Kristina Lerman (Eds.). ACM, 582–590

Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, and Xiangnan He. 2019 · 2019

Earlier work this paper cites.

Feature-level Deeper Self-Attention Network for Sequential Recommendation. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019 , Sarit Kraus (Ed.). ijcai.org, 4320–4326

Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, and Xiaofang Zhou. 2019 · 2019

Earlier work this paper cites.

Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 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.

On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 1748–1757

Walid Krichene and Steffen Rendle. 2020 · 2020

Earlier work this paper cites.

Oscar: Object-semantics aligned pre-training for vision-language tasks. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXX 16 . Springer, 121–137

Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al · 2020

Cited alongside, same era.

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. 2020 · 2020

Cited alongside, same era.

Long- and short-term self-attention network for sequential recommendation

Chengfeng Xu, Jian Feng, Pengpeng Zhao, Fuzhen Zhuang, Deqing Wang, Yanchi Liu, and Victor S. Sheng. 2021 · 2020

Cited alongside, same era.

Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020 , Jimmy X. Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1469–1478

Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality

Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023

Closest in time.

Palm-e: An embodied multimodal language model

Original

Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023

Closest in time.

Recommender Systems in the Era of Large Language Models (LLMs)

Original

Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023 · 2023

Closest in time.

Leveraging Large Language Models in Conversational Recommender Systems

Original

Luke Friedman, Sameer Ahuja, David Allen, Terry Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, et al · 2023

Closest in time.