2017 AMetaLearningPerspectiveonColdS

• (Vartak et al., 2017) ⇒ Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. (2017). “A Meta-Learning Perspective on Cold-Start Recommendations for Items.” In: Proceedings of Advances in Neural Information Processing Systems 30 (NIPS-2017).

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• http://scholar.google.com/scholar?q=%222017%22+A+Meta-Learning+Perspective+on+Cold-Start+Recommendations+for+Items

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Abstract

Matrix factorization (MF) is one of the most popular techniques for product recommendation, but is known to suffer from serious cold-start problems. Item cold-start problems are particularly acute in settings such as Tweet recommendation where new items arrive continuously. In this paper, we present a meta-learning strategy to address item cold-start when new items arrive continuously. We propose two deep neural network architectures that implement our meta-learning strategy. The first architecture learns a linear classifier whose weights are determined by the item history while the second architecture learns a neural network whose biases are instead adjusted. We evaluate our techniques on the real-world problem of Tweet recommendation. On production data at Twitter, we demonstrate that our proposed techniques significantly beat the MF baseline and also outperform production models for Tweet recommendation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 AMetaLearningPerspectiveonColdS	Hugo Larochelle Manasi Vartak Arvind Thiagarajan Conrado Miranda Jeshua Bratman			A Meta-Learning Perspective on Cold-Start Recommendations for Items						2017