Behavior Modeling Space Reconstruction for E-Commerce Search

TL;DR

This paper introduces the DRP framework for e-commerce search that reconstructs user behavior modeling space by removing relevance effects and adaptively fusing preferences, leading to improved search accuracy.

Contribution

The paper proposes a novel behavior modeling framework with preference editing and adaptive fusion to better separate preference and relevance effects in search systems.

Findings

Significant performance improvements over existing methods

Effective removal of relevance effects from preference predictions

Validated on multiple public and proprietary datasets

Abstract

Delivering superior search services is crucial for enhancing customer experience and driving revenue growth. Conventionally, search systems model user behaviors by combining user preference and query item relevance statically, often through a fixed logical 'and' relationship. This paper reexamines existing approaches through a unified lens using both causal graphs and Venn diagrams, uncovering two prevalent yet significant issues: entangled preference and relevance effects, and a collapsed modeling space. To surmount these challenges, our research introduces a novel framework, DRP, which enhances search accuracy through two components to reconstruct the behavior modeling space. Specifically, we implement preference editing to proactively remove the relevance effect from preference predictions, yielding untainted user preferences. Additionally, we employ adaptive fusion, which dynamically adjusts fusion criteria to align with the varying patterns of relevance and preference, facilitating more nuanced and tailored behavior predictions within the reconstructed modeling space. Empirical validation on two public datasets and a proprietary search dataset underscores the superiority of our proposed methodology, demonstrating marked improvements in performance over existing approaches.