ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation

The blackboard model of problem solving and the evolution of blackboard architectures

H Penny Nii. 1986 · 1986

Earlier work this paper cites.

Context-aware sequential recommendations withstacked recurrent neural networks. In The world wide web conference . 3172–3178

Lakshmanan Rakkappan and Vaibhav Rajan. 2019 · 2019

Earlier work this paper cites.

Choice modeling and assortment optimization in the presence of context effects

Reza Yousefi Maragheh, Xin Chen, James Davis, Jason Cho, Sushant Kumar, and Kannan Achan. 2020 · 2020

Earlier work this paper cites.

Prospect-net: Top-k retrieval problem using prospect theory. In 2022 IEEE International Conference on Big Data (Big Data) . IEEE, 3945–3951

Reza Yousefi Maragheh, Ramin Giahi, Jianpeng Xu, Lalitesh Morishetti, Shanu Vashishtha, Kaushiki Nag, Jason Cho, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2022 · 2022

Earlier work this paper cites.

Retrieval-augmented recommender system: Enhancing recommender systems with large language models. In Proceedings of the 17th ACM Conference on Recommender Systems . 1369–1373

Dario Di Palma. 2023 · 2023

Earlier work this paper cites.

Metagpt: Meta programming for multi-agent collaborative framework

Original

Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al · 2023

Earlier work this paper cites.

Seller-side Outcome Fairness in Online Marketplaces

Original

Zikun Ye, Reza Yousefi Maragheh, Lalitesh Morishetti, Shanu Vashishtha, Jason Cho, Kaushiki Nag, Sushant Kumar, and Kannan Achan. 2023 · 2023

Cited alongside, same era.

MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning

Original

Justin Chih-Yao Chen, Archiki Prasad, Swarnadeep Saha, Elias Stengel-Eskin, and Mohit Bansal. 2024 · 2024

Cited alongside, same era.

Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive Learning. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management . 404–414

Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, and Chen Ma. 2024 · 2024

Cited alongside, same era.

A review of modern recommender systems using generative models (gen-recsys). In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 6448–6458

Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, and Silvia Milano. 2024 · 2024

DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models

Original

Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu. 2024 · 2024

Later among the works it cites.

Coral: Collaborative retrieval-augmented large language models improve long-tail recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 3391–3401

Junda Wu, Cheng-Chun Chang, Tong Yu, Zhankui He, Jianing Wang, Yupeng Hou, and Julian McAuley. 2024 · 2024

Later among the works it cites.

Rankrag: Unifying context ranking with retrieval-augmented generation in llms

Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, and Bryan Catanzaro. 2024 · 2024

Later among the works it cites.

Addressing sparse data challenges in recommendation systems: A Systematic review of rating estimation using sparse rating data and profile enrichment techniques

Thennakoon Mudiyanselage Anupama Udayangani Gunathilaka, Prabhashrini Dhanushika Manage, Jinglan Zhang, Yuefeng Li, and Wayne Kelly. 2025 · 2025

Closest in time.