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Recommender Systems An Introduction.pdf下载
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2020-07-28 01:00:24
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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Recommender Systems An Introduction.pdf下载
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 相关下载链接://download.csdn.net/download/yumufenglin2009/5999917?utm_source=bbsseo
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Re
com
mender
Systems An
Int
roduct
ion
.
pdf
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
Building Re
com
mendat
ion
Engines
Building Re
com
mendat
ion
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 re
com
mendat
ion
engines that are personalized, scalable, and real time Get to grips with the best tool available on the market to create re
com
mender
systems This hands-on guide shows you how to implement different tools for re
com
mendat
ion
engines, and when to use which Book Descript
ion
A re
com
mendat
ion
engine (sometimes referred to as a re
com
mender
system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Re
com
mender
systems have be
com
e extremely
com
mon in recent years, and are applied in a variety of applicat
ion
s. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and p
roduct
s in general. The book starts with an
int
roduct
ion
to re
com
mendat
ion
systems and its applicat
ion
s. You will then start building re
com
mendat
ion
engines straight away from the very basics. As you move along, you will learn to build re
com
mender
systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. You will get an insight
int
o the pros and cons of each re
com
mendat
ion
engine and when to use which re
com
mendat
ion
to ensure each pick is the one that suits you the best. During the course of the book, you will create simple re
com
mendat
ion
engine, real-time re
com
mendat
ion
engine, scalable re
com
mendat
ion
engine, and more. You will familiarize yourselves with various techniques of re
com
mender
systems such as collaborative, content-based, and cross-re
com
mendat
ion
s before getting to know the best practices of building a re
com
mender
system towards the end of the book! What you will learn Build your first re
com
mendat
ion
engine Discover the tools needed to build re
com
mendat
ion
engines Dive
int
o the various techniques of re
com
mender
systems such as collaborative, content-based, and cross-re
com
mendat
ion
s Create efficient decis
ion
-making systems that will ease your work Familiarize yourself with machine learning algorithms in different frameworks Master different vers
ion
s of re
com
mendat
ion
engines from practical code examples Explore various re
com
mender
systems and implement them in popular techniques with R, Python, Spark, and others
Web and Big Data_First
Int
ernat
ion
al Jo
int
Conference, Part I-Springer(2017).
pdf
This volume (LNCS 10366) and its
com
pan
ion
volume (LNCS 10367) contain the proceedings of the first Asia-Pacific Web (APWeb) and Web-Age Informat
ion
Man- agement (WAIM) Jo
int
Conference on Web and Big Data, called APWeb-WAIM. This new jo
int
conference aims to attract participants from different scientific
com
munities as well as from industry, and not merely from the Asia Pacific reg
ion
, 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, informat
ion
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 applicat
ion
s in relat
ion
to Web informat
ion
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 be
com
e 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 submiss
ion
s to the research track and 19 to the demonstrat
ion
track, the conference accepted 44 regular (18%), 32 short research papers, and ten demon- strat
ion
s. 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 optimizat
ion
, topic modeling, machine learning, re
com
mender
systems, and distributed data processing. The technical program also included keynotes by Profs. Sihem Amer-Yahia (Nat
ion
al Center for Scientific Research, CNRS, France), Masaru Kitsuregawa (Nat
ion
al 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 contribut
ion
s to the conference program. As a new jo
int
conference, teamwork is particularly important for the success of APWeb-WAIM. We are deeply thankful to the Program
Com
mittee members and the external reviewers for lending their time and expertise to the conference. Special thanks go to the local Organizing
Com
mittee 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.
Machine.Learning.Refined.Foundat
ion
s.Algorithms.and.Applicat
ion
s.epub
Providing a unique approach to machine learning, this text contains fresh and
int
uitive, yet rigorous, descript
ion
s of all fundamental concepts necessary to conduct research, build p
roduct
s, tinker, and play. By prioritizing geometric
int
uit
ion
, algorithmic thinking, and practical real world applicat
ion
s in disciplines including
com
puter vis
ion
, natural language processing, economics, neuroscience, re
com
mender
systems, physics, and biology, this text provides readers with both a lucid understanding of foundat
ion
al material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based
com
putat
ion
al exercises and a
com
plete treatment of cutting edge numerical optimizat
ion
techniques, this is an essential resource for students and an ideal reference for researchers and practit
ion
ers working in machine learning,
com
puter science, electrical engineering, signal processing, and numerical optimizat
ion
. Addit
ion
al resources including supplemental discuss
ion
topics, code demonstrat
ion
s, and exercises can be found on the official textbook website at mlrefined.
com
Table of Contents Chapter 1
Int
roduct
ion
Part I Fundamental tools and concepts Chapter 2 Fundamentals of numerical optimizat
ion
Chapter 3 Regress
ion
Chapter 4 Classificat
ion
Part II Tools for fully data-driven machine learning Chapter 5 Automatic feature design for regress
ion
Chapter 6 Automatic feature design for classificat
ion
Chapter 7 Kernels, backpropagat
ion
, and regularized cross-validat
ion
Part III Methods for large scale machine learning Chapter 8 Advanced gradient schemes Chapter 9 Dimens
ion
reduct
ion
techniques Part IV Appendices Appendix A Basic vector and matrix operat
ion
s Appendix B Basics of vector calculus Appendix C Fundamental matrix factorizat
ion
s andthe pseudo-inverse Appendix D Convex geometry
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