Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This work proposes a personalized capping framework (PCap) to improve the diversity in Facebook Marketplace by introducing user-level diversity constraints at the retrieval stage, and uses an automated online optimization method called Parameter Tuning Sequence.
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.