Computational Trust Models and Machine Learning (Hardcover)

Xin Liu, Anwitaman Datta, Ee-Peng Lim

  • 出版商: CRC
  • 出版日期: 2014-11-04
  • 售價: $3,465
  • 貴賓價: 9.5$3,292
  • 語言: 英文
  • 頁數: 232
  • 裝訂: Hardcover
  • ISBN: 1482226669
  • ISBN-13: 9781482226669
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book:

  • Explains how reputation-based systems are used to determine trust in diverse online communities
  • Describes how machine learning techniques are employed to build robust reputation systems
  • Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly
  • Shows how decision support can be facilitated by computational trust models
  • Discusses collaborative filtering-based trust aware recommendation systems
  • Defines a framework for translating a trust modeling problem into a learning problem
  • Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions

Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

商品描述(中文翻譯)

《計算信任模型與機器學習》詳細介紹了信任的概念及其在各種計算機科學領域的應用,包括多智能體系統、在線社交網絡和通信系統。本書確定了傳統方法無法解決的信任建模挑戰,並提供以下內容:

- 解釋了基於聲譽的系統如何用於確定不同在線社區中的信任
- 描述了如何使用機器學習技術構建強大的聲譽系統
- 探討了兩種不同的方法來確定資源的可信度,一種是隱含人類角色,另一種是明確利用人類輸入
- 展示了計算信任模型如何促進決策支持
- 討論了基於協同過濾的信任感知推薦系統
- 定義了將信任建模問題轉化為學習問題的框架
- 調查了人類反饋的客觀性,強調過濾掉異常意見的必要性

《計算信任模型與機器學習》有效地展示了新穎的機器學習技術如何提高信任評估的準確性。