Fetching the paper…
Reading the bibliography…
This paper aims to address the challenge of sparse and missing data in recommendation systems, a significant hurdle in the age of big data.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, and Misha Smelyanskiy. 2019 · 1906
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Display Advertising Challenge
Olivier Chapelle Jean-Baptiste Tien, joycenv. 2014 · 2014
Earlier work this paper cites.
Missing data: Five practical guidelines
Daniel A Newman. 2014 · 2014
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Missing Data Imputation by LOLIMOT and FSVM/FSVR Algorithms with a Novel Approach: A Comparative Study. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations , Jesús Medina, Manuel Ojeda-Aciego, José Luis Verdegay, David A. Pelta, Inma P. Cabrera, Bernadette Bouchon-Meunier, and Ronald R. Yager (Eds.). Springer International Publishing, Cham, 551–569
Fatemeh Fazlikhani, Pegah Motakefi, and Mir Mohsen Pedram. 2018 · 2018
Earlier work this paper cites.
Data imputation using a trust network for recommendation via matrix factorization
Won Seok Hwang, Shu Li, Seung Woo Kim, and Keun Ho Lee. 2018 · 2018
Earlier work this paper cites.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. In NeurIPS EMC 2 Workshop
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
Earlier work this paper cites.
Missing Data Imputation for Geolocation-based Price Prediction Using KNN–MCF Method
Karshiev Sanjar, Olimov Bekhzod, Jaesoo Kim, Anand Paul, and Jeonghong Kim. 2020 · 2020
Earlier work this paper cites.
A Benchmark for Data Imputation Methods
Sebastian Jäger, Arndt Allhorn, and Felix Bießmann. 2021 · 2021
Earlier work this paper cites.
UDA: A user-difference attention for group recommendation
Shuxun Zan, Yujie Zhang, Xiangwu Meng, Pengtao Lv, and Yulu Du. 2021 · 2021
Earlier work this paper cites.
Design of recommendation system for tourist spot using sentiment analysis based on CNN-LSTM
Hyeon-woo An and Nammee Moon. 2022 · 2022
Earlier work this paper cites.
LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Earlier work this paper cites.
Automatically detecting groups using locality-sensitive hashing in group recommendations
Chintoo Kumar, C Ravindranath Chowdary, and Deepika Shukla. 2022 · 2022
Cited alongside, same era.
Movie recommender system (2022)
Nacho. 2022 · 2022
Cited alongside, same era.
Enumerating fair packages for group recommendations. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . 870–878
Ryoma Sato. 2022 · 2022
Cited alongside, same era.
Sequential group recommendations based on satisfaction and disagreement scores
Maria Stratigi, Evaggelia Pitoura, Jyrki Nummenmaa, and Kostas Stefanidis. 2022 · 2022
Cited alongside, same era.
Knowledge graph-based multi-context-aware recommendation algorithm
Chao Wu, Sannyuya Liu, Zeyu Zeng, Mao Chen, Adi Alhudhaif, Xiangyang Tang, Fayadh Alenezi, Norah Alnaim, and Xicheng Peng. 2022 · 2022
Cited alongside, same era.
GPT-NER: Named Entity Recognition via Large Language Models
Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, and Guoyin Wang. 2023 · 2023
Later among the works it cites.
Empirical Study of Zero-Shot NER with ChatGPT. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 7935–7956
Tingyu Xie, Qi Li, Jian Zhang, Yan Zhang, Zuozhu Liu, and Hongwei Wang. 2023 · 2023
Later among the works it cites.
Multimodal LLMs for Health Grounded in Individual-Specific Data. In Machine Learning for Multimodal Healthcare Data , Andreas K. Maier, Julia A. Schnabel, Pallavi Tiwari, and Oliver Stegle (Eds.). Springer Nature Switzerland, Cham, 86–102
Anastasiya Belyaeva, Justin Cosentino, Farhad Hormozdiari, Krish Eswaran, Shravya Shetty, Greg Corrado, Andrew Carroll, Cory Y. McLean, and Nicholas A. Furlotte. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
GBERT: Pre-training user representations for ephemeral group recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 2631–2639
Song Zhang, Nan Zheng, and Danli Wang. 2022 · 2022
Cited alongside, same era.
Two-level graph path reasoning for conversational recommendation with user realistic preference. In proceedings of the 31st ACM international conference on information & knowledge management . 2701–2710
Rongmei Zhao, Shenggen Ju, Jian Peng, Ning Yang, Fanli Yan, and Siyu Sun. 2022 · 2022
Cited alongside, same era.
Explainable session-based recommendation with meta-path guided instances and self-attention mechanism. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2555–2559
Jiayin Zheng, Juanyun Mai, and Yanlong Wen. 2022 · 2022
Cited alongside, same era.
LLM Based Generation of Item-Description for Recommendation System. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys ’23) . Association for Computing Machinery, New York, NY, USA, 1204–1207
Arkadeep Acharya, Brijraj Singh, and Naoyuki Onoe. 2023 · 2023
Cited alongside, same era.
The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?
Ângela Jornada Ben, Johanna M. van Dongen, Mohamed El Alili, Martijn W. Heymans, Jos W. R. Twisk, Janet L. MacNeil-Vroomen, Maartje de Wit, Susan E. M. van Dijk, Teddy Oosterhuis, and Judith E. Bosmans. 2023 · 2023
Cited alongside, same era.
Language Models are Realistic Tabular Data Generators. In The Eleventh International Conference on Learning Representations
Vadim Borisov, Kathrin Sessler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. 2023 · 2023
Cited alongside, same era.
News Summarization and Evaluation in the Era of GPT-3
Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2023 · 2023
Cited alongside, same era.
Qixin Deng, Qikai Yang, Ruibin Yuan, Yipeng Huang, Yi Wang, Xubo Liu, Zeyue Tian, Jiahao Pan, Ge Zhang, Hanfeng Lin, et al · 2024
Closest in time.
Learning from teaching regularization: Generalizable correlations should be easy to imitate
Can Jin, Tong Che, Hongwu Peng, Yiyuan Li, and Marco Pavone. 2024a · 2024
Closest in time.
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Can Jin, Hongwu Peng, Shiyu Zhao, Zhenting Wang, Wujiang Xu, Ligong Han, Jiahui Zhao, Kai Zhong, Sanguthevar Rajasekaran, and Dimitris N Metaxas. 2024b · 2024
Closest in time.
A Vehicle Classification Method Based on Machine Learning
Xinjin Li, Jinghao Chang, Tiexin Li, Wenhan Fan, Yu Ma, and Haowei Ni. 2024a · 2024
Closest in time.
Intelligent Vehicle Classification System Based on Deep Learning and Multi-Sensor Fusion
Xinjin Li, Yuanzhe Yang, Yixiao Yuan, Haowei Ni, Yu Ma, and Yangchen Huang. 2024b · 2024
Closest in time.
LLM-generated Explanations for Recommender Systems. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (Cagliari, Italy) (UMAP Adjunct ’24) . Association for Computing Machinery, New York, NY, USA, 276–285
Sebastian Lubos, Thi Ngoc Trang Tran, Alexander Felfernig, Seda Polat Erdeniz, and Viet-Man Le. 2024 · 2024
Closest in time.
Research on the Application of Support Vector Machine Algorithm Model With Multi-Modal Data Fusion in Breast Cancer Ultrasound Image Classification
Lai Peng and Qian Leng. 2024 · 2024
Closest in time.
GPT-Signal: Generative AI for Semi-automated Feature Engineering in the Alpha Research Process.. In Proceedings of the Joint Workshop of the 8th Financial Technology and Natural Language Processing, and the 1st Workshop on Agent AI for Scenario Planning @ IJCAI 2024
Yining Wang, Jinman Zhao, and Yuri Lawryshyn. 2024 · 2024
Closest in time.
NExT-GPT: Any-to-Any Multimodal LLM
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. 2024 · 2024
Closest in time.
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang. 2024 · 2024
Closest in time.