[Editor’s note] This account is the official account for the first recommendation of the fourth paradigm intelligent recommendation product. This account is based on the computer field, especially the cutting-edge research related to artificial intelligence. It aims to share more knowledge related to artificial intelligence with the public and promote the public’s understanding of ARTIFICIAL intelligence from a professional perspective. At the same time, we also hope to provide an open platform for discussion, communication and learning for people related to ARTIFICIAL intelligence, so that everyone can enjoy the value created by artificial intelligence as soon as possible.


Recommendation system has been a hot research direction in academic circles. Many students and researchers want to get into the recommender system, but they have no way to start because there are too many and miscellaneous related materials in this field. This paper collected and sorted out books, public courses, conferences, technical blogs, project codes related to recommendation system, and finally gave a brief example of the application of recommendation system in different fields.

Outline:

1. Introductory books
2. Introductory tutorial
3. Open data set
4. Project code
5. Technical post
6. Academic conference
7. Application field

Recommended System Introduction books:

1. Practice of Recommendation System by Xiang Liang

Getting started is preferred. This book is the first book about recommendation system in China, which can let you quickly know how to apply the theoretical knowledge learned to practice, how to apply programming ability to the recommendation system. The code listed in the book has its fair share of drawbacks. Highly recommended!

2. Collective Intelligence Programming (Programming Collective Intelligence)

This book is ideal for readers who have relatively little knowledge of mathematics but want to dig deeper into the field, or practitioners who have real project needs but don’t have enough time to dig deeper. The author of the book is very visually shows a large amount of classical algorithm of artificial intelligence and machine learning, and more importantly, the authors show the algorithm can representative examples are used in the Internet, in many cases, also combined with some actual operation data for further elucidation of the Web site easily understood. Combining learning with machine learning-related courses will get twice the result with half the effort.

3. Recommendation Systems: Technology, Evaluation, and Efficient Algorithms (Recommender Systems Handbook)

Authors: Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor

This book is known by many as the “pillow book”. The book, which has more than 600 pages, is currently in its second edition and a Chinese translation has been published. This book is a must for anyone who wants to keep recommendations as a research direction! This book in the form of a special topic, related to all aspects of the recommendation system. Each project will list the papers involved in the project and the future development trend, which can be a good guide, not only as an introduction to the theory, but also as a data index for a specific problem.

4. Recommendation System (Recommender Systems: An Introduction)

Authors: Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich

This book is comprehensive in content and relatively simple in theory, without too many difficult formulas. The biggest advantage of this book is that it makes a good arrangement and summary of the recommendation system, almost summarizes every module involved in the recommendation system, and gives readers a good recommendation engine architecture lesson. After reading this book, I can basically have a clear understanding and relatively complete control of the recommendation system.

There was an interview with the Turing community about the book:



Dietmar Jannach and Professor Gerhard Friedrich on the Latest Research direction of recommendation System(2013)

5. Music Recommendation and Discovery

By Oscar Celma

This book takes music recommendation as the content, and gives a general introduction to the needs and problems, common practices and effect evaluation of music recommendation. The content of effect evaluation is worth reading carefully.

6. Word Sense Disambiguation: Algorithms and Applications

This book explores the issue of word sense disambiguation in a comprehensive way, including important algorithms, approaches, indicators, results, philosophical issues and applications, as well as a comprehensive overview of the history and development of the field by leading scholars in the field. If keyword recommendations or text recommendations are involved, check out this book.

Recommended system introduction tutorial:

Introduction to recommendation System:

www.coursera.org/specializat…

This course is published by the University of Minnesota and consists of five courses, namely introduction of recommendation systems, Nearest Neighbor Collaborative Filtering, Evaluation of recommendation systems, Matrix decomposition, and achievement of recommendation systems.

Recommended system open data sets:

1. Amazon
2. Alibaba
3. Retail Rocket
4. Book Crossing
5. Netflix
6. Movie Lens
7. CiaoDVD
8. Film Trust
9. Yahoo Music
10. Amazon Music
11. LastFM
12. Million Song Dataset
13. Steam Video Games
14. Jester
15. Chicago Entree
16. Anime
17. SNAP
18. Grouplens
19. Yahoo Research
20. LibRec

Recommendation System project code:

Mrec(Python)

Github.com/mendeley/mr…

Crab(Python)

Github.com/muricoca/cr…

Python-recsys(Python)

Github.com/ocelma/pyth…

CofiRank(C++)

Github.com/markusweime…

GraphLab(C++)

Github.com/graphlab-co…

EasyRec(Java)

Github.com/hernad/easy…

Lenskit(Java)

Github.com/grouplens/l…

Mahout(Java)

Github.com/apache/maho…

Recommendable(Ruby)

Github.com/davidcelis/…

Recommendation system related technical blog:

1. Blog. Sciencenet. Cn/home. PHP? Mo…

2. Weibo.com/p/100505168…

3. Zhan.renren.com/recommender…

4. groups.google.com/forum/#! The for…

5. www.cnblogs.com/LeftNotEasy

6. lovebingkuai.diandian.com/

7. blog.pluskid.org/

8. Benanne. Making. IO / 2014/08/05 /…

9. glinden.blogspot.com/

10. aimotion.blogspot.com/

11. Graphlab.org/lsrs2013/pr…

12. www.cnblogs.com/flclain/

Academic conferences related to recommendation system:

AAAI : The National Conference of the American Association for Artificial Intelligence

ACM RecSys : The ACM Conference Series on Recommender Systems

ACM SIGKDD : The ACM SIGKDD Conference on Knowledge Discovery and Data Mining

ACM SIGIR : The ACM International Conference on Research and Development in Information Retrieval

ACM CIKM : The ACM International Conference on Information and Knowledge Management

ICDM : The IEEE International Conference on Data Mining

ICML : The International Conference on Machine Learning

IJCAI : The International Joint Conference on Artificial Intelligence

NIPS: The Conference on Neural Information Processing Systems

NIPS: The Conference on Neural Information Processing Systems

SDM : The SIAM International Conference on Data Mining

WSDM : The International Conference on Web Search and Data Mining

WWW :The International World Wide Web Conference

Application examples of recommendation system in different fields:

1. Book video: Netflix, Youtube, MovieLens, Douban, netease Cloud Music
2. News: Google News, Toutiao, Zhihu, Hulu
3. Social networking: Facebook, Twitter, Weibo, Renren
4. Travel: Wanderfly, TripAdvisor, Mafengwo, Where to
5. E-commerce retail: Amazon, Taobao, Tmall, JINGdong

The above content is published by the fourth paradigm – first recommendation, only for learning exchange, copyright belongs to the original author.

Welcome everyone to like, collect, share more technical dry goods with friends around.

Related reading:

Dry goods | five research hot spots of personalized recommendation system can explain recommended (5)

Dry goods | five research hot spots of personalized recommendation system user portrait (4)

Reinforcement learning of five Research hotspots of personalized Recommendation System (III)

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