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Text-based recommendation holds a wide range of practical applications due to its versatility, as textual descriptions can represent nearly any type of item.
Language models are few-shot learners
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Characterizing microblogs with topic models
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On-demand feature recommendations derived from mining public product descriptions
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Linked open data to support content-based recommender systems
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Bpr: Bayesian personalized ranking from implicit feedback
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The movielens datasets: History and context
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Efficient algorithms for personalized pagerank
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Mining affective text to improve social media item recommendation
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Joint text embedding for personalized content-based recommendation
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Scientific article recommendation by using distributed representations of text and graph
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Neural collaborative filtering
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How useful are reviews for recommendation? a critical review and potential improvements
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MIND: A large-scale dataset for news recommendation
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Enhancing explicit and implicit feature interactions via information sharing for parallel deep ctr models
Bo Chen, Yichao Wang, Zhirong Liu, Ruiming Tang, Wei Guo, Hongkun Zheng, Weiwei Yao, Muyu Zhang, and Xiuqiang He. 2021 · 2021
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A review of content-based and context-based recommendation systems
Umair Javed, Kamran Shaukat, Ibrahim A Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo. 2021 · 2021
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Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021 · 2021
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Introducing linked open data in graph-based recommender systems
Cataldo Musto, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2017 · 2017
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Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
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Semantics-aware recommender systems exploiting linked open data and graph-based features
Cataldo Musto, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2018 · 2018
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Dissecting contextual word embeddings: Architecture and representation
Matthew E Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih. 2018 · 2018
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Explainable recommendation via multi-task learning in opinionated text data
Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018 · 2018
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Improving text-based similar product recommendation for dynamic product advertising at yahoo
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Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
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Elevater: A benchmark and toolkit for evaluating language-augmented visual models
Chunyuan Li, Haotian Liu, Liunian Li, Pengchuan Zhang, Jyoti Aneja, Jianwei Yang, Ping Jin, Houdong Hu, Zicheng Liu, Yong Jae Lee, et al. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al. 2022 · 2022
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When large language models meet personalization: Perspectives of challenges and opportunities
Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, et al. 2023 · 2023
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Palr: Personalization aware llms for recommendation
Zheng Chen. 2023 · 2023
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How can recommender systems benefit from large language models: A survey
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How do text-davinci-002 and text-davinci-003 differ?
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Towards open-world recommendation with knowledge augmentation from large language models
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
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