Off-policy Learning in Two-stage Recommender Systems

WWW '20: The Web Conference 2020 Taipei Taiwan April, 2020, pp. 463-473, 2020.

Cited by: 12|Views122
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Abstract:

Many real-world recommender systems need to be highly scalable: matching millions of items with billions of users, with milliseconds latency. The scalability requirement has led to widely used two-stage recommender systems, consisting of efficient candidate generation model(s) in the first stage and a more powerful ranking model in the se...More

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