3MN: Three Meta Networks for Multi-Scenario and Multi-Task Learning in Online Advertising Recommender Systems
Yifei Zhang, Hua Hua, Hui Guo, Shuangyang Wang, Chongyu Zhong, Shijie Zhang · 2023
Recommender systems are widely applied on web. For example, online advertising systems rely on recommender systems to accurately estimate the value of display opportunities, which is critical to maximize the profits of advertisers. To reduce computational resource consumption, the core tactic of Multi-Scenario Multi-Task Learning (MSMTL) is to devise a single recommder system that is adapted to all contexts instead of implementing multiple scenario-oriented or task-oriented recommender systems. However, MSMTL is challenging because there are complicated task-task, scenario-scenario, and task-scenario interrelations; the characteristic of different tasks in different scenarios also largely varies; and samples of each context are often unevenly distributed. Previous MSMTL solutions focus on applying scenario knowledge to improve the performance of multi-task learning, while neglecting the complicated interrelations among tasks and scenarios. Moreover, samples derived from different scenarios are transferred into the latent embedding with the same dimension. This static embedding strategy impedes the practicality of model expressiveness, since the scenarios with sufficient samples are underrepresented and those with insufficient samples are over-represented.