Item Cluster-aware Prompt Learning for Session-based Recommendation

TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval . 1921–1924

Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2020 · 1924

Earlier work this paper cites.

Fast unfolding of communities in large networks

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008 · 2008

Earlier work this paper cites.

Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)

Original

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

Inter-session modeling for session-based recommendation. In Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems . 24–31

Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, and Helge Langseth. 2017 · 2017

Earlier work this paper cites.

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18) . Association for Computing Machinery, New York, NY, USA, 843–852

Balázs Hidasi and Alexandros Karatzoglou. 2018 · 2018

Earlier work this paper cites.

STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1831–1839

Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018 · 2018

Earlier work this paper cites.

Streaming session-based recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 1569–1577

Lei Guo, Hongzhi Yin, Qinyong Wang, Tong Chen, Alexander Zhou, and Nguyen Quoc Viet Hung. 2019 · 2019

Earlier work this paper cites.

Performance comparison of neural and non-neural approaches to session-based recommendation. In Proceedings of the 13th ACM conference on recommender systems . 462–466

Malte Ludewig, Noemi Mauro, Sara Latifi, and Dietmar Jannach. 2019 · 2019

Earlier work this paper cites.

From Louvain to Leiden: guaranteeing well-connected communities

Vincent A Traag, Ludo Waltman, and Nees Jan Van Eck. 2019 · 2019

Earlier work this paper cites.

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

Earlier work this paper cites.

Graph contextualized self-attention network for session-based recommendation.. In IJCAI , Vol. 19. 3940–3946

Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019 · 2019

Earlier work this paper cites.

Language models are few-shot learners

Original

Tom B Brown. 2020 · 2020

Cited alongside, same era.

Handling information loss of graph neural networks for session-based recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 1172–1180

Tianwen Chen and Raymond Chi-Wing Wong. 2020 · 2020

Cited alongside, same era.

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

Cited alongside, same era.

Exploiting cross-session information for session-based recommendation with graph neural networks

Ruihong Qiu, Zi Huang, Jingjing Li, and Hongzhi Yin. 2020 · 2020

Cited alongside, same era.

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 4222–4235

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

Later among the works it cites.

Learning to prompt for vision-language models

Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022 · 2022

Later among the works it cites.

Domain adaptation via prompt learning

Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, and Gao Huang. 2023 · 2023

Later among the works it cites.

Exploiting explicit and implicit item relationships for session-based recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining . 553–561

Zihao Li, Xianzhi Wang, Chao Yang, Lina Yao, Julian McAuley, and Guandong Xu. 2023 · 2023

Later among the works it cites.

Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023b · 2023