SELECTING CONTENT ITEMS USING REINFORCEMENT LEARNING

• Main Authors: CRESPO, Jean-Francois, MANN, Timothy, SULEYMAN, Mustafa, CHU, Chia-Yueh Carlton, COPPIN, Benjamin Kenneth, SZUMMER, Martin, WALTERS, Thomas Chadwick, RUS, Luis Carlos Cobo
• Format: Patent
• Language: English; French; German
• Published: 22.01.2020
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a machine learning model that has been trained through reinforcement learning to select a content item. One of the methods includes receiving first data characterizing a first context in which a first content item may be presented to a first user in a presentation environment; and providing the first data as input to a long-term engagement machine learning model, the model having been trained through reinforcement learning to: receive a plurality of inputs, and process each of the plurality of inputs to generate a respective engagement score for each input that represents a predicted, time-adjusted total number of selections by the respective user of future content items presented to the respective user in the presentation environment if the respective content item is presented in the respective context.