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I am beginning to build a recommendation system. I have users on a website and they purchase services, so I'll recommend services that commonly go along - i.e. are purchased by a single user (not necessarily at the same time).

What are good introductory papers & readings on the subject? What are the most common or easier to learn algorithms and approaches to this problem?

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    $\begingroup$ possible duplicate of Matrix Factorization algorithms for Recommender Systems $\endgroup$ – Antoine Aug 27 '15 at 8:58
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    $\begingroup$ I'm not convinced these questions are duplicates: in particular I'm not sure that Dawny's answer would fit the other question. But they are very similar and no doubt people who find this page would be interested in the other, and vice versa. $\endgroup$ – Silverfish Aug 27 '15 at 12:33
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    $\begingroup$ These are not duplicates, in my opinion. One is about papers, the other is about specific algorithms $\endgroup$ – Peter Flom Aug 27 '15 at 12:53
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Recommender systems is a very nice topic and has some really nice tutorials and courses on the internet.

One of my favorites is Programming Collective Intelligence by Toby Segaran (2007) which gives a hands on implementation of basic recommender systems(in Python). This book would help you get comfortable with the basic concepts.

This paper by Linden et al. (2011) explains the item based collaborative filtering algorithm of Amazon.

This is a really nice introductory course on recommender systems, on Coursera.

This is also a very nice book for getting started at recommender systems, which takes the reader from a beginner to an expert level. Here is the link to it on Springer (publication website of the book)

But, I recommend you start with the "Programming Collective Intelligence" book, so that you can get an idea about converting the recommender systems algorithms into code, which would help you in translating(into code) the concepts in the other resources too.


Linden, G., Smith, B., and York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.

Ricci, F., Rokach, L., Shapira, B., and Kantor, P.B. (2011). Introduction to Recommender Systems Handbook. Springer.

Segaran, T. (2007). Programming Collective Intelligence: Building Smart Web 2.0 Applications. O'Reilly.

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In addition to reading papers, I'd suggest you take a look at some code. Mahout, among others, has recommender algortihms built in, which are fairly simple to understand and illustrate the main concepts.

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Besides those already metioned resources I would add, this chapter of the "Mining Massive Datasets" book. It is also a very good starting point. It clearly explains the two main approaches to recommender systems. It also covers the main results from the Netflix challenge and has tips on how to implement these types of systems.

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You can have a look into :

Waila, P.; Singh, V.; Singh, M. (26 April 2016). "A Scientometric Analysis of Research in Recommender Systems" (PDF). Journal of Scientometric Research: 71–84. doi:10.5530/jscires.5.1.1 [PDF]

Paper gives details of the Recommender system research from different dimensions.

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