DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
This addresses the trade-off between ad revenue and user experience in online platforms like e-commerce and news feeds, representing an incremental improvement over existing RL-based advertising methods.
The paper tackles the problem of balancing advertising revenue and user experience in recommender systems by proposing a deep reinforcement learning framework that jointly decides whether to insert an ad, which ad to choose, and where to place it, with experimental results on real-world data showing effectiveness.
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.