piRank: A Probabilistic Intent Based Ranking Framework for Facebook Search
Zhen Liao

TL;DR
piRank is a scalable probabilistic intent-based ranking framework designed for Facebook Search, addressing data quality issues and combining machine learning with empirical methods to improve ranking effectiveness and system agility.
Contribution
Introduces a novel probabilistic intent-based ranking framework that enhances scalability, debuggability, and combines ML with empirical algorithms for Facebook Search.
Findings
Effective in handling tail query intents
Improves ranking system development speed
Validated through extensive experiments on Facebook Search
Abstract
While numerous studies have been conducted in the literature exploring different types of machine learning approaches for search ranking, most of them are focused on specific pre-defined problems but only a few of them have studied the ranking framework which can be applied in a commercial search engine in a scalable way. In the meantime, existing ranking models are often optimized for normalized discounted cumulative gains (NDCG) or online click-through rate (CTR), and both types of machine learning models are built based on the assumption that high-quality training data can be easily obtained and well applied to unseen cases. In practice at Facebook search, we observed that our training data for ML models have certain issues. First, tail query intents are hardly covered in our human rating dataset. Second, search click logs are often noisy and hard to clean up due to various reasons.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWeb Data Mining and Analysis · Information Retrieval and Search Behavior · Recommender Systems and Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
