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A Meta-Learning Perspective on Cold-Start Recommendations for Items
Manasi Vartak · Arvind Thiagarajan · Conrado Miranda · Jeshua Bratman · Hugo Larochelle

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #72

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.

Author Information

Manasi Vartak (Massachusetts Institute of Technology)
Arvind Thiagarajan (Twitter)
Conrado Miranda
Jeshua Bratman (Twitter)
Hugo Larochelle (Google Brain)

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