Click-through rate (CTR) prediction aims to predict the probability that the
user will click an item, which has been one of the key tasks in online
recommender and advertising systems. In such systems, rich user behavior (viz.
long- and short-term) has been proved to be of great value in capturing user
interests. Both industry and academy have paid much attention to this topic and
propose different approaches to modeling with long-term and short-term user
behavior data. But there are still some unresolved issues. More specially, (1)
rule and truncation based methods to extract information from long-term
behavior are easy to cause information loss, and (2) single feedback behavior
regardless of scenario to extract information from short-term behavior lead to
information confusion and noise. To fill this gap, we propose a Graph based
Long-term and Short-term interest Model, termed GLSM. It consists of a
multi-interest graph structure for capturing long-term user behavior, a
multi-scenario heterogeneous sequence model for modeling short-term
information, then an adaptive fusion mechanism to fused information from
long-term and short-term behaviors. Comprehensive experiments on real-world
datasets, GLSM achieved SOTA score on offline metrics. At the same time, the
GLSM algorithm has been deployed in our industrial application, bringing 4.9%
CTR and 4.3% GMV lift, which is significant to the business.

本文提出一种名为 GLSM 的基于图形的长短期兴趣模型，它包含用于捕捉长期用户行为的多兴趣图形结构，用于建模短期信息的多场景异构序列模型以及自适应融合机制来融合长期和短期行为的信息，并在实际应用中展示出了显著的 CTR 和 GMV 提升。