Deep reinforcement learning for personalized search story recommendation

Jason Zhang, Junming Yin, Dongwon Lee, Linhong Zhu

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, search story, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms. The recommended search story guides a user to identify her own preference and personal intent, which subsequently influences the user's real-time and long-term search behavior. As search stories become increasingly important, in this work, we study the problem of personalized search story recommendation within a search engine, which aims to suggest a search story relevant to both a search keyword and an individual user's interest. To address the challenge of modeling both immediate and future values of recommended search stories (i.e., cross-channel effect), for which conventional supervised learning framework is not applicable, we resort to a Markov decision process and propose a deep reinforcement learning architecture trained by both imitation learning and reinforcement learning. We empirically demonstrate the effectiveness of our proposed approach through extensive experiments on real-world data sets from JD.com.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Jul 26 2019

ASJC Scopus subject areas

  • General

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