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In this work, we address the problem of transfer learning for sequential recommendation model. Most of the state-of-the-art recommendation systems consider user preference and give customized results to different users. However, for those users without enough data, personalized recommendation systems cannot infer their preferences well or rank items precisely. Recently, transfer learning techniques...
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