Cross DQN: Cross Deep Q Network for Ads Allocation in Feed

• URL: http://arxiv.org/abs/2109.04353v1
• Date: Thu, 9 Sep 2021 15:44:13 GMT
• Title: Cross DQN: Cross Deep Q Network for Ads Allocation in Feed
• Authors: Guogang Liao, Ze Wang, Xiaoxu Wu, Xiaowen Shi, Chuheng Zhang, Yongkang Wang, Xingxing Wang, Dong Wang
• Abstract summary: E-commerce platforms usually display a mixed list of ads and organic items in feed. We propose Cross Deep Q Network to extract the arrangement signal by crossing the embeddings of different items. Our model results in higher revenue and better user experience than state-of-the-art baselines in offline experiments.
• Score: 11.752703912138944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: E-commerce platforms usually display a mixed list of ads and organic items in feed. One key problem is to allocate the limited slots in the feed to maximize the overall revenue as well as improve user experience, which requires a good model for user preference. Instead of modeling the influence of individual items on user behaviors, the arrangement signal models the influence of the arrangement of items and may lead to a better allocation strategy. However, most of previous strategies fail to model such a signal and therefore result in suboptimal performance. To this end, we propose Cross Deep Q Network (Cross DQN) to extract the arrangement signal by crossing the embeddings of different items and processing the crossed sequence in the feed. Our model results in higher revenue and better user experience than state-of-the-art baselines in offline experiments. Moreover, our model demonstrates a significant improvement in the online A/B test and has been fully deployed on Meituan feed to serve more than 300 millions of customers.

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