Addressing cold-start problem in recommendation systems
By Xuan Nhat Lam, Thuc D. Vu, Trong Duc Le, Anh Duc Duong. International Conference on Ubiquitous Information Management and Communication, 2008
489 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/612ab2c052c11c9f797234481dbbbdc82bc7cd68
How researchers see this paper
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In Computer Vision and Pattern Recognition
- Mohammad Rostami
- Vincent Wenchen Zheng
- Fernando Ortega
- Antonio Hernando
- Yongfeng Zhang
- Shijie Geng
- Jesús Bobadilla
- Zuohui Fu
In Information Systems
- Mohammad Rostami
- Fernando Ortega
- Antonio Hernando
- Abraham Gutiérrez
- Zuohui Fu
- Jesús Bobadilla
- Yingqiang Ge
- Shijie Geng
In Signal Processing
- Jesús Bobadilla
- Mohammad Rostami
- Bin Guo
- Fernando Ortega
- Antonio Hernando
- Jesus Bernal
- Yongfeng Zhang
- Hugo Larochelle
Citations in the corpus
- A collaborative filtering approach to mitigate the new user cold start problem (2012, background): “The new community problem [49,27] refers to the difficulty in obtaining, when starting up a RS, a sufficient amount of data (ratings) which enable reliable recommendations to be made.”
- Matchbox: large scale online bayesian recommendations (2009, unknown): “In order to measure accuracy in a cold start situation we adopt the methodology of [11] and [13].”
- CityTransfer (2018, background): “However, when a chain enterprise plans to expand its market in a new city, it usually lacks enough data which can be directly referenced, known as the cold-start problem [6,24].”
- A crowdsourcing triage algorithm for geopolitical event forecasting (2018, methodology): “Moreover, after learning the user skills, we can use the current framework to solve the cold start problem for new tasks [6].”
- A Meta-Learning Perspective on Cold-Start Recommendations for Items (2017, background): “In order to address the cold-start problem, work such as [3, 14, 27, 17] has extended the MF model so that user- and item-specific terms can be included in their respective representations.”
- Recommender systems survey (2013, background): “The new community problem [204,129] refers to the difficulty, when starting up a RS, in obtaining, a sufficient amount of data (ratings) for making reliable recommendations.”
- Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) (2022, background): “One widely studied problem under this setting is the cold-start recommendation where users [24] or items [50] are new to the system with no previous interaction records.”