Year: 2020
Type: conference-paper
Abstract: Online recommendation and advertising are two major income channels for online recommendation platforms (e.g. e-commerce and news feed site). However, most platforms optimize recommending and advertising strategies by different teams separately via different techniques, which may lead to suboptimal overall performances. To this end, in this paper, we propose a novel two-level reinforcement learning framework to jointly optimize the recommending and advertising strategies, where the first level generates a list of recommendations to optimize user experience in the long run; then the second level inserts ads into the recommendation list that can balance the immediate advertising revenue from advertisers and the negative influence of ads on long-term user experience. To be specific, the first level tackles high combinatorial action space problem that selects a subset items from the large item space; while the second level determines three internally related tasks, i.e., (i) whether to insert an ad, and if yes, (ii) the optimal ad and (iii) the optimal location to insert. The experimental results based on real-world data demonstrate the effectiveness of the proposed framework. We have released the implementation code to ease reproductivity.
Institutions: Michigan State University, ByteDance (China)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: III: Small: Collaborative Research: Effective Labeled Data Generation via Generative Adversarial Learning, CAREER: Real-World Networks: Modeling and Analysis of Signed Networks with Positive and Negative Links, SaTC: CORE: Small: Side-channel Attacks Against Mobile Users: Singularity Detection, Behavior Identification, and Automated Rectification, FW-HTF-RM: Intelligent Social Network Interventions to Augment Human Cognition for Interdisciplinary Interactions in Project Teams, III: Small: Unsupervised Feature Selection in the Era of Big Data, III: Small: Collaborative Research: A General Feature Learning Framework for Dynamic Attributed Networks
OpenAlex ID: W3007094061