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Poster
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
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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.

Keywords: One-Shot/Low-Shot Learning Approaches · Recommender Systems · Learning to Learn
[ Paper] 

Author Information

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

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