相關主題
商品描述
Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales.
On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.
商品描述(中文翻譯)
受到各大內容平台（書籍、音樂、電影）和線上拍賣網站的讚譽，推薦系統是數位策略的關鍵元素。如果開發最初是為了提升資訊系統的效能，現在的問題已經大幅轉向客戶關係的邏輯優化，主要目標是最大化潛在銷售。
在跨學科的方式下，推薦引擎和推薦系統結合了資訊科學與傳播學、行銷學、社會學、數學和計算機科學的貢獻。它探討了推薦系統背後模型的理解，並描述了其歷史背景。它還分析了這些系統在內容提供中的發展，並評估了它們對用戶行為的影響。
