PRISM: Refracting the Entangled User Behavior Space for E-Commerce Search

TL;DR

PRISM is a novel framework for e-commerce search that explicitly models the interaction between user preferences and item relevance, improving robustness and semantic alignment in behavior prediction.

Contribution

It introduces a preference-relevance interaction model with modules for preference rectification, semantic anchoring, and adaptive evidence routing, addressing behavioral confounding.

Findings

PRISM outperforms strong baselines on two public benchmarks.

Explicit modeling of preference-relevance interaction enhances robustness.

Semantic anchoring improves relevance representation calibration.

Abstract

E-commerce search systems rely on modeling user behavior to estimate item relevance and user preference, which are typically assumed to be stable and independently learnable signals. However, in practice, user interactions are jointly shaped by exposure mechanisms, feedback loops, and semantic matching, leading to entangled and dynamically drifting behavioral signals. As a result, both preference estimation and relevance modeling suffer from confounding effects and semantic misalignment, which limits the robustness of downstream ranking models. To address this issue, we propose PRISM, a Preference-Relevance Interaction Semantic Modeling framework for e-commerce search behavior prediction. PRISM explicitly models the interaction between user preference and item relevance rather than treating them as independent components. Specifically, it introduces a preference rectification module to iteratively refine user preference under relevance-aware constraints, improving robustness against behavioral confounding. To ensure semantic consistency, we further incorporate a large language model (LLM)-driven semantic anchoring mechanism that leverages positive and negative prototypes to calibrate relevance representations. Finally, a preference-conditioned evidence routing module adaptively aggregates multi-source behavioral signals, enabling context-aware and preference-aligned relevance estimation. Extensive experiments on two public e-commerce benchmarks demonstrate that PRISM consistently outperforms strong baselines, validating the effectiveness of explicitly modeling preference-relevance interaction for robust and semantically grounded search behavior modeling.