Problem
Research questions and friction points this paper is trying to address.
e-commerce search
long-term user value
granularity gap
multi-objective fusion
ranking score
DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search
🤖 AI Summary
本文提出DCEO方法，通过直接优化因果效应来解决电商搜索中长期用户价值建模问题，改善了目标对齐度和个人化水平。
📝 Abstract
Industrial e-commerce search systems ultimately aim to optimize the user-level long-term objective, such as n-day cumulative purchases or gross merchandise value (GMV) per user. However, such objectives are defined at the user level, whereas search ranking is based on item-level scores within each request. Existing methods typically bridge this granularity gap through manually designed multi-objective fusion, where predictions of multiple item-level objectives, such as clicks, carts, purchases, and transaction value, are combined into a ranking score that serves as a proxy for the ultimate objective. Such hand-crafted fusion schemes rely on a small set of manually tuned weights, limiting fine-grained personalization and leading to suboptimal alignment with the ultimate objective. In this paper, we propose DCEO (Direct Causal Effect Optimization), a data-driven framework for learning item-level proxy scores that are better aligned with the ultimate objective. We first aggregate the item-level proxy scores into a user-level proxy metric and quantify its alignment with the ultimate objective using a relative causal effect. We then develop an actor-critic framework, where the critic estimates the ultimate objective for a given user-level proxy metric, and the actor dynamically generates context-dependent fusion weights over multiple objectives to construct the item-level proxy scores and is trained to directly optimize the relative causal effect. Extensive offline experiments and analyses demonstrate the effectiveness and interpretability of DCEO. In addition, DCEO has been deployed in a large-scale industrial e-commerce search system, outperforming the conventional GMV proxy by 0.36% in GMV in a 41-day online A/B test.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Direct Causal Effect Optimization
actor-critic framework
relative causal effect
multi-objective fusion
🔎 Similar Papers
No similar papers found.
