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A Meta-Learning Perspective on Cold-Start Recommendations for Items

Manasi Vartak
Arvind Thiagarajan
Conrado Miranda
Jeshua Bratman
NIPS (2017)


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 {\it 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 adapted based on item history. 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 with lookup table based user embeddings and also outperform the state-of-the-art production model for Tweet recommendation.

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