PCap: Personalized Retrieval-Stage Diversity Capping in Facebook Marketplace

2026-09-14

AI Summary

为提高Facebook Marketplace的多样性，提出PCap框架，通过用户级多样性约束和个性化类别限制，并利用在线优化方法调整参数，改善用户体验。

Abstract

We propose a personalized capping framework (PCap) to improve the diversity in Facebook Marketplace by introducing user-level diversity constraints at the retrieval stage. PCap models individual diversity preferences using Shannon entropy-based scoring, segments users into diversity buckets, and applies personalized category caps during multi-source candidate retrieval. To navigate the high-dimensional parameter space of per-bucket caps, we leverage an automated online optimization method called Parameter Tuning Sequence. Large-scale online experiments demonstrate that PCap significantly improves users' browsing experience shown in engagement metrics. This work provides practical insights into integrating personalized diversity into industrial retrieval systems.

Problem

• diversity
• retrieval stage
• personalized capping
• user experience

Innovation

• Personalized Capping Framework
• Shannon Entropy-based Scoring
• Diversity Buckets
• Parameter Tuning Sequence
• Multi-source Candidate Retrieval