Recommender Systems An Introduction.pdf下载

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推荐系统比较好的综述，把行业内的推荐分析了一遍，外文教材
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推荐系统比较好的综述，把行业内的推荐分析了一遍，外文教材

Practical Recommender Systems.pdf推荐系统推荐系统的书

Building Recommendation Engines English | 5 Jan. 2017 | ISBN: 1785884859 | 357 Pages | AZW3/MOBI/EPUB/PDF (conv) | 81.07 MB Key Features A step-by-step guide to building recommendation engines that are personalized, scalable, and real time Get to grips with the best tool available on the market to create recommender systems This hands-on guide shows you how to implement different tools for recommendation engines, and when to use which Book Description A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general. The book starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best. During the course of the book, you will create simple recommendation engine, real-time recommendation engine, scalable recommendation engine, and more. You will familiarize yourselves with various techniques of recommender systems such as collaborative, content-based, and cross-recommendations before getting to know the best practices of building a recommender system towards the end of the book! What you will learn Build your first recommendation engine Discover the tools needed to build recommendation engines Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations Create efficient decision-making systems that will ease your work Familiarize yourself with machine learning algorithms in different frameworks Master different versions of recommendation engines from practical code examples Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and others

This volume (LNCS 10366) and its companion volume (LNCS 10367) contain the proceedings of the first Asia-Pacific Web (APWeb) and Web-Age Information Man- agement (WAIM) Joint Conference on Web and Big Data, called APWeb-WAIM. This new joint conference aims to attract participants from different scientific communities as well as from industry, and not merely from the Asia Pacific region, but also from other continents. The objective is to enable the sharing and exchange of ideas, experiences, and results in the areas of World Wide Web and big data, thus covering Web tech- nologies, database systems, information management, software engineering, and big data. The first APWeb-WAIM conference was held in Beijing during July 7–9, 2017. As a new Asia-Pacific flagship conference focusing on research, development, and applications in relation to Web information management, APWeb-WAIM builds on the successes of APWeb and WAIM: APWeb was previously held in Beijing (1998), Hong Kong (1999), Xi’an (2000), Changsha (2001), Xi’an (2003), Hangzhou (2004), Shanghai (2005), Harbin (2006), Huangshan (2007), Shenyang (2008), Suzhou (2009), Busan (2010), Beijing (2011), Kunming (2012), Sydney (2013), Changsha (2014), Guangzhou (2015), and Suzhou (2016); and WAIM was held in Shanghai (2000), Xi’an (2001), Beijing (2002), Chengdu (2003), Dalian (2004), Hangzhou (2005), Hong Kong (2006), Huangshan (2007), Zhangjiajie (2008), Suzhou (2009), Jiuzhaigou (2010), Wuhan (2011), Harbin (2012), Beidaihe (2013), Macau (2014), Qingdao (2015), and Nanchang (2016). With the fast development of Web-related technologies, we expect that APWeb-WAIM will become an increasingly popular forum that brings together outstanding researchers and developers in the field of Web and big data from around the world. The high-quality program documented in these proceedings would not have been possible without the authors who chose APWeb-WAIM for disseminating their find- ings. Out of 240 submissions to the research track and 19 to the demonstration track, the conference accepted 44 regular (18%), 32 short research papers, and ten demon- strations. The contributed papers address a wide range of topics, such as spatial data processing and data quality, graph data processing, data mining, privacy and semantic analysis, text and log data management, social networks, data streams, query pro- cessing and optimization, topic modeling, machine learning, recommender systems, and distributed data processing. The technical program also included keynotes by Profs. Sihem Amer-Yahia (National Center for Scientific Research, CNRS, France), Masaru Kitsuregawa (National Institute of Informatics, NII, Japan), and Mohamed Mokbel (University of Minnesota, Twin Cities, USA) as well as tutorials by Prof. Reynold Cheng (The University of Hong Kong, SAR China), Prof. Guoliang Li (Tsinghua University, China), Prof. Arijit Khan (Nanyang Technological University, Singapore), and VI Preface Prof. Yu Zheng (Microsoft Research Asia, China). We are grateful to these distin- guished scientists for their invaluable contributions to the conference program. As a new joint conference, teamwork is particularly important for the success of APWeb-WAIM. We are deeply thankful to the Program Committee members and the external reviewers for lending their time and expertise to the conference. Special thanks go to the local Organizing Committee led by Jun He, Yongxin Tong, and Shimin Chen. Thanks also go to the workshop co-chairs (Matthias Renz, Shaoxu Song, and Yang-Sae Moon), demo co-chairs (Sebastian Link, Shuo Shang, and Yoshiharu Ishikawa), industry co-chairs (Chen Wang and Weining Qian), tutorial co-chairs (Andreas Züfle and Muhammad Aamir Cheema), sponsorship chair (Junjie Yao), proceedings co-chairs (Xiang Lian and Xiaochun Yang), and publicity co-chairs (Hongzhi Yin, Lei Zou, and Ce Zhang). Their efforts were essential to the success of the conference. Last but not least, we wish to express our gratitude to the Webmaster (Zhao Cao) for all the hard work and to our sponsors who generously supported the smooth running of the conference. We hope you enjoy the exciting program of APWeb-WAIM 2017 as documented in these proceedings.

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