pytorch中的训练模型示例 PyTorch中的深度学习算法的一些实现。 排名-学习排名 排名网 前馈NN，最小化文档对交叉熵损失函数 训练模型 python ranking/RankNet.py --lr 0.001 --debug --standardize --debug打印参数规范和参数grad规范。 这可以评估是否存在梯度消失和梯度爆炸问题- --standardize可确保将输入缩放为平均值为0且标准差为1.0 NN结构：136-> 64-> 16-> 1，ReLU6作为激活函数 优化器 r 时代 损失（火车） 损失（评估） ndcg @ 10 ndcg @ 30 秒/纪元 因式分解 对/秒 亚当 0.001 25 0.63002 0.635508 0.41785 0.49337 312 损失函数 203739 亚当 0.001 50 0.6

TensorFlow2.0中Ranknet到LambdaRank的实现 **此存储库在Tensorflow 2.0中具有从RankNet到LambdaRank的实现，** 要求 tqdm == 4.32.1 numpy == 1.16.4 点击== 7.0 tensorflow_gpu == 2.1.0 设置 $ git clone https://github.com/akanyaani/ranknet-tensorflow2.0 $ cd ranknet-tensorflow2.0 $ pip install -r requirements.txt 从此处下载数据，并将任何折叠传递给pre_process。 $ python pre_process.py --help Options: --data-dir TEXT training data

本资料主要是辅助learning to rank学习，目前比较靠谱的工具是RankLib,故使用它来学习learning to rank。文件夹包括RankLib-2.10.jar，测试数据集合MQ2008，测试文件格式说明文档，Tie－Yan Liu － Learning to Rank for Information Retrieval.pdf

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, the acquisition of image quality scores has several limitations: 1) scores are not precise, because subjects are usually uncertain about which score most precisely represents the perceptual quality of a given image; 2) subjective judgments of quality may be biased by image content; 3) the quality scales between different distortion categories are inconsistent, because images corrupted by different types of distortion are evaluated independently in subjective experiments; and 4) it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting sufficient images associated with different kinds of distortion that have diverse levels of degradation, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as “the quality of image Ia is better than that of image Ib” for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable performance

-Learning to Rank for Information Retrieval Tieyan Liu, 2011, book

elasticsearch-learning-to-rank-es_7_6_2.zip

Learning to Rank tools Ranklib就是一套优秀的Learning to Rank领域的开源实现，其主页在：http://people.cs.umass.edu/~vdang/ranklib.html，从主页中可以看到实现了哪些方法。其中由微软发布的LambdaMART是IR业内常用的Learning to Rank模型

LTR的经典书籍。系统介绍了机器学习排序的各种算法。刘铁岩编著。

DeepRank Learning to rank with neural networks for recommendation.pdf

刘铁岩 排序 信息检索 很经典的书，找了好久