Which Tricks Are Important for Learning to Rank?
Nowadays, state-of-the-art learning-to-rank methods are based on gradient-boosted decision trees (GBDT). The most well-known algorithm is LambdaMART which was proposed more than a decade ago. Recently, several other GBDT-based ranking algorithms were proposed. In this paper, we thoroughly analyze these methods in a unified setup. In particular, we address the following questions. Is direct optimization of a smoothed ranking loss preferable over optimizing a convex surrogate? How to properly construct and smooth surrogate ranking losses? To address these questions, we compare LambdaMART with YetiRank and StochasticRank methods and their modifications. We also propose a simple improvement of the YetiRank approach that allows for optimizing specific ranking loss functions. As a result, we gain insights into learning-to-rank techniques and obtain a new state-of-the-art algorithm.
Code (0)
등록된 구현이 없습니다.
Tasks
Learning-To-RankSimilar Papers 제목 키워드 기반
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving more attention. Training data extraction is therefore of great importance, as it can serve as a potential tool to assess privacy leakage. However, due …
Text GenerationBag of Tricks and A Strong Baseline for Deep Person Re-identification
This paper explores a simple and efficient baseline for person re-identification (ReID). Person re-identification (ReID) with deep neural networks has made progress and achieved high performance in recent years. However,…
Person Re-IdentificationA Brand-level Ranking System with the Customized Attention-GRU Model
In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is …
Feature EngineeringA Benchmark on Tricks for Large-scale Image Retrieval
Many studies have been performed on metric learning, which has become a key ingredient in top-performing methods of instance-level image retrieval. Meanwhile, less attention has been paid to pre-processing and post-proce…
Image RetrievalMetric LearningRetrievalBag of Tricks for Retail Product Image Classification
Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we prese…
ClassificationGeneral Classificationimage-classificationImage Classification