Find all the Top AIRetail papers. Links to pdf, code repos and demos are provided.
Two-stage recommender systems are widely adopted in industry due to their scalability and maintainability. These systems produce recommendations in tw...
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We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ...
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Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest. ...
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The proliferation of massive datasets combined with the development of
sophisticated analytical techniques have enabled a wide variety of novel
applic...
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When building recommendation systems, we seek to output a helpful set of items to the user. Under the hood, a ranking model predicts which of two cand...
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The explosion of e-commerce has caused the need for processing and analysis of product titles, like entity typing in product titles. However, the rapi...
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Standard lossy image compression algorithms aim to preserve an image's appearance, while minimizing the number of bits needed to transmit it. However,...
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Provides an overview of Pinterest's visual discovery engine, which enhances user engagement through innovative visual search and recommendation tools....
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In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder r...
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Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items....
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Most real-world networks exhibit community structure, a phenomenon
characterized by existence of node clusters whose intra-edge connectivity is
strong...
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Introduces Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to enhance recommender systems by effectively utilizin...
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