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Recommender Systems: An Introduction Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich
Publisher: Cambridge University Press

The authors then introduced a number of "item re-ranking methods that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Its interface is clean and the tools are very easy to use. (Note the findings about the suitability of a particular algorithm and about user perspectives on lists of results). There is no glitch in any transaction. A wish for recommender system at Expedia. Recommendations are a part of everyday life. This hands-on course is suitable for software engineers, data analysts and statisticians. Introduction to Data Science – Building Recommender Systems … January 29, 2013 | Filed under: Data Science. In particular, we introduce a design principle by focusing on the dynamic relationship between the recommender sys- tem's performance and the number of new training samples the system requires. Introduction to Recommender Systems Handbook. Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. Index Terms—machine learning, recommender systems, supervised learning, nearest neighbor, classification. ň�发现另一本介绍推荐系统的好书Recommender Systems:An Introduction （第一本是Recommender system handbook），找了很久才找到地址，给大家分享一下（下载地址在文章末尾）。 本书的目录如下：. Share ebook Recommender Systems: An Introduction (repost). Until recently, this literature suggests, research on recommendation systems has focused almost exclusively on accuracy, which led to systems that were likely to recommend only popular items, and hence suffered from a "popularity bias'' (Celma and Herrera 2008). Andreas Geyer-Schulz, Uni Karlsruhe In a rather German introduction, he noted that one of the main goals of having a recommender system is to save both the time of the user and the staff member. This report presents a general introduction to the topic and discusses major emerging challenges. The fourth and final speaker was Sean Owen, founder at Myrrix, a startup that is building complete, real-time, scalable recommender system, built on Apache Mahout. The Recommender Stammtisch is a meetup for people who are interested in recommender systems, user behavior analytics, machine learning, AI and related topics. This blog entry introduces a state-of-the-art report written by Sirris on recommender systems.