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Recent advancements in Large Language Models (LLMs) have shown significant potential in enhancing recommender systems.
Identifying manipulated offerings on review portals. In Proceedings of the 2013 conference on empirical methods in natural language processing . 1933–1942
Jiwei Li, Myle Ott, and Claire Cardie. 2013 · 1942
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Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference . 689–698
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020 · 2020
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Multimodal Review Generation with Privacy and Fairness Awareness. In Proceedings of the 28th International Conference on Computational Linguistics , Donia Scott, Nuria Bel, and Chengqing Zong (Eds.). International Committee on Computational Linguistics, Barcelona, Spain (Online), 414–425
Xuan-Son Vu, Thanh-Son Nguyen, Duc-Trong Le, and Lili Jiang. 2020 · 2020
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Collaborative filtering and kNN based recommendation to overcome cold start and sparsity issues: A comparative analysis
Taushif Anwar, V Uma, Md Imran Hussain, and Muralidhar Pantula. 2022 · 2022
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Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems (Seattle, WA, USA) (RecSys ’22) . Association for Computing Machinery, New York, NY, USA, 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
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Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22) . Association for Computing Machinery, New York, NY, USA, 2595–2600
Xu Zhao, Yi Ren, Ying Du, Shenzheng Zhang, and Nian Wang. 2022 · 2022
Cited alongside, same era.
Uncovering ChatGPT’s Capabilities in Recommender Systems. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys ’23) . Association for Computing Machinery, New York, NY, USA, 1126–1132
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.
Recommender systems in the era of large language models (llms)
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023 · 2023
Cited alongside, same era.
Large language models as zero-shot conversational recommenders. In Proceedings of the 32nd ACM international conference on information and knowledge management . 720–730
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys ’23) . Association for Computing Machinery, New York, NY, USA, 890–896
Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, and Lucas Dixon. 2023 · 2023
Later among the works it cites.
Rethinking the evaluation for conversational recommendation in the era of large language models
Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Contrastive Collaborative Filtering for Cold-Start Item Recommendation. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW ’23) . Association for Computing Machinery, New York, NY, USA, 928–937
Zhihui Zhou, Lilin Zhang, and Ning Yang. 2023 · 2023
Later among the works it cites.
Large Language Models are Zero-Shot Rankers for Recommender Systems. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part II (Glasgow, United Kingdom). Springer-Verlag, Berlin, Heidelberg, 364–381
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Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. 2023 · 2023
Cited alongside, same era.
Diffusion augmentation for sequential recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 1576–1586
Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, and Feng Tian. 2023 · 2023
Cited alongside, same era.
Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al · 2023
Cited alongside, same era.
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024 · 2024
Closest in time.
Large Language Models as Data Augmenters for Cold-Start Item Recommendation
Jianling Wang, Haokai Lu, James Caverlee, Ed Chi, and Minmin Chen. 2024 · 2024
Closest in time.