Statistical Methods for Recommender Systems(Hardcover)

Deepak K. Agarwal, Bee-Chung Chen

  • 出版商: Cambridge
  • 出版日期: 2016-02-24
  • 售價: $2,100
  • 貴賓價: 9.5$1,995
  • 語言: 英文
  • 頁數: 298
  • 裝訂: Hardcover
  • ISBN: 1107036070
  • ISBN-13: 9781107036079
  • 相關分類: 推薦系統
  • 相關翻譯: 統計推薦系統 (簡中版)
  • 立即出貨 (庫存 < 3)

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商品描述

Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

商品描述(中文翻譯)

設計算法來為使用者推薦新聞文章和電影等項目,在許多網路應用中是一項具有挑戰性的任務。問題的關鍵在於根據使用者對不同項目的反應來排名項目,以優化多個目標。主要的技術挑戰包括使用稀疏數據進行高維度預測,以及構建高維度的連續設計來收集用戶建模和系統設計的數據。這本關於推薦系統中出現的統計問題的全面論述,包括對當前最先進方法的詳細深入討論,例如適應性連續設計(多臂擲硬幣方法)、雙線性隨機效應模型(矩陣分解)以及使用MapReduce等現代計算範式進行可擴展模型擬合。作者們借鑒了他們在Yahoo!和LinkedIn等大規模系統上的豐富經驗,通過與他們直接參與的應用示例來闡明複雜概念,將理論與實踐之間的鴻溝拉近。