STAR: A Simple Training-free Approach for Recommendations using Large Language Models

Language models are few-shot learners

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Session-based recommendations with recurrent neural networks

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Image-based recommendations on styles and substitutes

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Universal sentence encoder for English

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Self-attentive sequential recommendation

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Personalized top-n sequential recommendation via convolutional sequence embedding

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Hierarchical gating networks for sequential recommendation

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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer

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Feature-level deeper self-attention network for sequential recommendation

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Accelerating large-scale inference with anisotropic vector quantization

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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models

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Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith B Hall, Daniel Cer, and Yinfei Yang. 2021 · 2021

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Popularity bias in dynamic recommendation

Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee. 2021 · 2021

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Zero shot recommender systems

Hao Ding, Anoop Deoras, Yuyang (Bernie) Wang, and Hao Wang. 2022 · 2022

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Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)

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Towards universal sequence representation learning for recommender systems

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Holistic evaluation of language models

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Tallrec: An effective and efficient tuning framework to align large language model with recommendation

Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023

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Uncovering chatgpt’s capabilities in recommender systems

Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023 · 2023

Cited alongside, same era.

An unified search and recommendation foundation model for cold-start scenario

Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, and Guannan Zhang. 2023 · 2023

Large language models are zero-shot rankers for recommender systems

Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024 · 2024

Closest in time.

Enhancing sequential recommendation via llm-based semantic embedding learning

Jun Hu, Wenwen Xia, Xiaolu Zhang, Chilin Fu, Weichang Wu, Zhaoxin Huan, Ang Li, Zuoli Tang, and Jun Zhou. 2024 · 2024

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Large language models meet collaborative filtering: An efficient all-round llm-based recommender system

Original

Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, and Chanyoung Park. 2024 · 2024

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Gecko: Versatile text embeddings distilled from large language models

Original

Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R Cole, Kai Hui, Michael Boratko, Rajvi Kapadia, Wen Ding, et al. 2024 · 2024

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Enhancing id and text fusion via alternative training in session-based recommendation