DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems

Authors

  • Xiangyu Zhao Michigan State University
  • Changsheng Gu Bytedance
  • Haoshenglun Zhang Bytedance
  • Xiwang Yang Bytedance
  • Xiaobing Liu Bytedance
  • Jiliang Tang Michigan State University
  • Hui Liu Michigan State University

Keywords:

Business/Marketing/Advertising/E-commerce, Web Personalization & User Modeling, Web Search & Information Retrieval

Abstract

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce and news feed sites). However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos). Developing an optimal advertising algorithm in recommendations faces immense challenges because interpolating ads improperly or too frequently may decrease user experience, while interpolating fewer ads will reduce the advertising revenue. Thus, in this paper, we propose a novel advertising strategy for the rec/ads trade-off. To be specific, we develop an RL-based framework that can continuously update its advertising strategies and maximize reward in the long run. Given a recommendation list, we design a novel Deep Q-network architecture that can determine three internally related tasks jointly, i.e., (i) whether to interpolate an ad or not in the recommendation list, and if yes, (ii) the optimal ad and (iii) the optimal location to interpolate. The experimental results based on real-world data demonstrate the effectiveness of the proposed framework.

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Published

2021-05-18

How to Cite

Zhao, X., Gu, C., Zhang, H., Yang, X., Liu, X., Tang, J., & Liu, H. (2021). DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 750-758. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16156

Issue

Section

AAAI Technical Track on Application Domains