Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search

TL;DR

This paper introduces a novel framework for assessing the true value of consultations in personalized search, moving beyond semantic similarity to improve search relevance in e-commerce AI assistants.

Contribution

It proposes a new consultation value assessment framework and a value-aware personalized search model called VAPS that better captures consultation importance for personalization.

Findings

VAPS outperforms baseline models in retrieval tasks.

The framework effectively identifies high-value consultations.

Experiments on multiple datasets validate the approach.

Abstract

Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of 'value' labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for personalization. To address this, we propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. Based on this, we introduce VAPS, a value-aware personalized search model that selectively incorporates high-value consultations through a consultation-user action interaction module and an explicit objective that aligns consultations with user actions. Experiments on both public and commercial datasets show that VAPS consistently outperforms baselines in both retrieval and ranking tasks.