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Sequential Recommender Systems (SRS), which model a user's interaction history to predict the next item of interest, are widely used in various applications.
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. 1901 · 1901
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K.; van Merriënboer, B.; Gulçehre, Ç.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014 · 2014
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Qualitatively characterizing neural network optimization problems
Goodfellow, I. J.; Vinyals, O.; and Saxe, A. M. 2014 · 2014
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Session-based recommendations with recurrent neural networks
Hidasi, B.; Karatzoglou, A.; Baltrunas, L.; and Tikk, D. 2016 · 2016
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Self-attentive sequential recommendation
Kang, W.-C.; and McAuley, J. 2018 · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Tang, J.; and Wang, K. 2018 · 2018
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Deep interest network for click-through rate prediction
Zhou, G.; Zhu, X.; Song, C.; Fan, Y.; Zhu, H.; Ma, X.; Yan, Y.; Jin, J.; Li, H.; and Gai, K. 2018 · 2018
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Explainable fashion recommendation: a semantic attribute region guided approach
Hou, M.; Wu, L.; Chen, E.; Li, Z.; Zheng, V. W.; and Liu, Q. 2019 · 2019
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BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer
Sun, F.; Liu, J.; Wu, J.; Pei, C.; Lin, X.; Ou, W.; and Jiang, P. 2019 · 2019
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Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations
Fang, H.; Zhang, D.; Shu, Y.; and Guo, G. 2020 · 2020
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Category-aware collaborative sequential recommendation
Cai, R.; Wu, J.; San, A.; Wang, C.; and Wang, H. 2021 · 2021
Cited alongside, same era.
Contrastive self-supervised sequential recommendation with robust augmentation
Liu, Z.; Chen, Y.; Li, J.; Yu, P. S.; McAuley, J.; and Xiong, C. 2021 · 2021
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2022 · 2022
Cited alongside, same era.
Filter-enhanced MLP is all you need for sequential recommendation
Zhou, K.; Yu, H.; Zhao, W. X.; and Wen, J.-R. 2022 · 2022
Cited alongside, same era.
Tallrec: An effective and efficient tuning framework to align large language model with recommendation
Bao, K.; Zhang, J.; Zhang, Y.; Wang, W.; Feng, F.; and He, X. 2023 · 2023
Cited alongside, same era.
Multi-task recommendations with reinforcement learning
Liu, Z.; Tian, J.; Cai, Q.; Zhao, X.; Gao, J.; Liu, S.; Chen, D.; He, T.; Zheng, D.; Jiang, P.; et al. 2023d · 2023
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Decentralized collaborative learning framework for next POI recommendation
Long, J.; Chen, T.; Nguyen, Q. V. H.; and Yin, H. 2023 · 2023
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Understanding and modeling passive-negative feedback for short-video sequential recommendation
Pan, Y.; Gao, C.; Chang, J.; Niu, Y.; Song, Y.; Gai, K.; Jin, D.; and Li, Y. 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
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Batchsampler: Sampling mini-batches for contrastive learning in vision, language, and graphs
Yang, Z.; Huang, T.; Ding, M.; Dong, Y.; Ying, R.; Cen, Y.; Geng, Y.; and Tang, J. 2023 · 2023
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Leveraging large language models for sequential recommendation
Harte, J.; Zorgdrager, W.; Louridas, P.; Katsifodimos, A.; Jannach, D.; and Fragkoulis, M. 2023 · 2023
Cited alongside, same era.
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation
Kim, K.; Hyun, D.; Yun, S.; and Park, C. 2023 · 2023
Cited alongside, same era.
Automlp: Automated mlp for sequential recommendations
Li, M.; Zhang, Z.; Zhao, X.; Wang, W.; Zhao, M.; Wu, R.; and Guo, R. 2023b · 2023
Cited alongside, same era.
Mmmlp: Multi-modal multilayer perceptron for sequential recommendations
Liang, J.; Zhao, X.; Li, M.; Zhang, Z.; Wang, W.; Liu, H.; and Liu, Z. 2023 · 2023
Cited alongside, same era.
Llara: Aligning large language models with sequential recommenders
Liao, J.; Li, S.; Yang, Z.; Wu, J.; Yuan, Y.; Wang, X.; and He, X. 2023 · 2023
Cited alongside, same era.
STRec: Sparse Transformer for Sequential Recommendations
Li, C.; Wang, Y.; Liu, Q.; Zhao, X.; Wang, W.; Wang, Y.; Zou, L.; Fan, W.; and Li, Q. 2023a
Cited in the paper.
Li, X.; Chen, C.; Zhao, X.; Zhang, Y.; and Xing, C. 2023c
Cited in the paper.
Later among the works it cites.
A survey of large language models
Zhao, W. X.; Zhou, K.; Li, J.; Tang, T.; Wang, X.; Hou, Y.; Min, Y.; Zhang, B.; Zhang, J.; Dong, Z.; et al. 2023 · 2023
Later among the works it cites.
A survey on evaluation of large language models
Chang, Y.; Wang, X.; Wang, J.; Wu, Y.; Yang, L.; Zhu, K.; Chen, H.; Yi, X.; Wang, C.; Wang, Y.; et al. 2024 · 2024
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Enhancing sequential recommendation via llm-based semantic embedding learning
Hu, J.; Xia, W.; Zhang, X.; Fu, C.; Wu, W.; Huan, Z.; Li, A.; Tang, Z.; and Zhou, J. 2024 · 2024
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NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
Lee, C.; Roy, R.; Xu, M.; Raiman, J.; Shoeybi, M.; Catanzaro, B.; and Ping, W. 2024 · 2024
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Prototypical contrastive learning through alignment and uniformity for recommendation
Ou, Y.; Chen, L.; Pan, F.; and Wu, Y. 2024 · 2024
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