Preference Learning

偏好学习是机器学习的一个子领域,专注于通过已知偏好信息建立预测模型。主要任务包括偏好表达(如效用函数、部分/整体排名)、用户/物品描述等。排名误差的衡量方式有斯皮尔曼简捷法、Kendall's距离等。此外,文章还探讨了加权排名误差、二分排名问题以及多种偏好学习技术,如学习效用函数、构建偏好关系等。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Introduction

维基百科中对Preference Learning的解释是这样的:通过已知的可观测的偏好信息构建一个偏好预测模型。偏好学习是机器学习的一个子领域,并且它的主要任务是学会进行排名(”learning to rank”)。

  • 这张图显示了人工智能和偏好学习的关系以及偏好学习的应用领域

ML和PL的关系

  • 这张图显示了Preference 在人工智能中的应用

PL的应用

Preference Learning Task

偏好学习问题可以从好几个维度去学习:

  • 偏好表达:
    • 效用函数(utility function):数值型表达,或者按顺序表达
    • 偏好关系:部分/整体排名(Ranking)
    • 逻辑表示法…
  • 用户/物品的描述:
    • 标识符,特征向量,结构化对象…
  • 训练数据集的类型:
    • 直接/间接的信息反馈
    • 完整/不完整的关系
    • 公用程式(utilities)

1)偏好表达

  • 偏好的评估是绝对的,可以是二值型也可以是数值型或者枚举型的效用函数表达法。
  • 偏好的比较是相对的,采用的是部分排名或者整体排名,是偏好关系表达法。

偏好表达

2)用户/物品的描述

  • 在多标签分类问题中用户的描述是二值型数据,0表示不喜欢,1表示喜欢等。
  • 它的预测结果也是二分类的数值。

多标签分类

  • 在多标签排名问题中用户的描述是二值型数据
  • 它的预测结果数值型的按顺序表达的偏好,1表示最喜欢。

多标签排名

  • 在标签排名问题中用户的描述是一个排名
  • 它的预测结果数值型的按顺序表达的偏好,1表示最喜欢。

标签排名

  • 下面这个是标准的标签排名问题。
  • 结合了相对的和绝对的排名。

标准

  • 实例(用户)排名,根据用户的特征对用户进行排名。

### Embedding Preference Implementation and Models in Machine Learning Embeddings are a type of word representation that allows words with similar meaning to have a similar representation. These representations can be used as inputs into neural networks, enhancing performance on various tasks such as natural language processing (NLP), recommendation systems, and more. In the context of automating the end-to-end lifecycle of machine learning applications[^1], embedding models play an essential role by transforming raw data into meaningful features automatically. For implementing embeddings effectively: #### Selecting Appropriate Embedding Techniques Different types of embeddings cater to specific needs within machine learning projects. Word2Vec, GloVe, FastText, BERT, among others, offer varying levels of complexity and capabilities depending upon project requirements. Each technique has its strengths when it comes to capturing semantic relationships between entities like words or items in collaborative filtering scenarios. For instance, pre-trained transformer-based architectures like BERT provide state-of-the-art results across many NLP benchmarks due to their ability to understand contextual nuances better than traditional static vector approaches. #### Implementing Customizable Preferences Within Embedding Layers When building custom solutions using deep learning frameworks such as TensorFlow/Keras or PyTorch, one may define preferences through hyperparameters controlling aspects including dimensionality reduction methods applied during training phases; initialization schemes chosen for weight matrices associated with each layer involved in generating vectors from input sequences; regularization strategies employed against overfitting risks posed by high-dimensional spaces where these mappings reside. Below is an example demonstrating how customizable parameters could look while defining an embedding layer inside Keras API: ```python from tensorflow.keras.layers import Embedding vocab_size = 5000 # Size of vocabulary embedding_dim = 16 # Dimension of dense embedding space embed_layer = Embedding(input_dim=vocab_size, output_dim=embedding_dim, mask_zero=True, trainable=True) ``` This code snippet initializes an `Embedding` object configured according to specified criteria regarding size constraints imposed both at entry points (`input_dim`) alongside exit ones (`output_dim`). Additionally, options exist here concerning whether zero values should act as padding indicators via `mask_zero`, along with setting up layers' adaptability post-initialization phase thanks to `trainable`. #### Training Strategies for Effective Utilization of Embeddings To ensure optimal utilization of learned representations throughout subsequent stages following extraction processes conducted earlier – fine-tuning procedures often prove beneficial especially whenever transfer learning paradigms come under consideration since they allow leveraging previously acquired knowledge bases accumulated elsewhere before adapting them locally towards target domains characterized uniquely based off dataset characteristics present therein. Moreover, employing techniques aimed specifically toward mitigating issues related closely around sparsity concerns arising out of large vocabularies might involve applying subword units instead which break down infrequent tokens into smaller components thereby reducing overall memory footprint required whilst maintaining quality intact simultaneously too. --related questions-- 1. What factors influence choosing different kinds of embedding algorithms? 2. How does one determine appropriate dimensions for embedding spaces given particular datasets? 3. Can you explain some common pitfalls encountered during embedding training and ways to avoid them? 4. In what situations would someone prefer non-trainable versus trainable embeddings within neural network designs?
评论 3
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值