Deep Bayesian Multi-Target Learning for Recommender Systems
This work addresses the need for balancing multiple evaluation criteria like click-through rate and user engagement in e-commerce platforms, representing an incremental advancement in multi-target learning methods.
The authors tackled the problem of multi-target optimization in recommender systems by introducing Deep Bayesian Multi-Target Learning (DBMTL), which models target events as a Bayesian network with hidden layers, and applied it to Taobao live-streaming recommendation, resulting in significant improvements over other multi-task learning frameworks and non-multi-task models.
With the increasing variety of services that e-commerce platforms provide, criteria for evaluating their success become also increasingly multi-targeting. This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL). In this framework, target events are modeled as forming a Bayesian network, in which directed links are parameterized by hidden layers, and learned from training samples. The structure of Bayesian network is determined by model selection. We applied the framework to Taobao live-streaming recommendation, to simultaneously optimize (and strike a balance) on targets including click-through rate, user stay time in live room, purchasing behaviors and interactions. Significant improvement has been observed for the proposed method over other MTL frameworks and the non-MTL model. Our practice shows that with an integrated causality structure, we can effectively make the learning of a target benefit from other targets, creating significant synergy effects that improve all targets. The neural network construction guided by DBMTL fits in with the general probabilistic model connecting features and multiple targets, taking weaker assumption than the other methods discussed in this paper. This theoretical generality brings about practical generalization power over various targets distributions, including sparse targets and continuous-value ones.