References

Paper Title and Link	Author	Summary	Year
Evaluating Recommendation Systems	Shani and Gunawardana	Describes various recommendation systems performance metrics and when to use them	2009
Hybrid Recommender Systems: Survey and Experiments	Robin Burke	A very readable summary of the different types of recommender systems	2002
Recommender Systems Handbook	Various	A comprehensive handbook outlining the recommender system space	2010
Item-Based Collaborative Filtering Recommendation Algorithms	Sarwar, Karypis, Konstan, and Riedl	Overview of Item-Item algorithms, and a good overview of many collaborative filtering techniques	2001
Collaborative Filtering for Implicit Feedback Datasets	Hu,Koren, and Volinksy	A deep dive into a CF algorithm tailored for implicit feedback recommenders. MLlib ALS implemented the algorithm described in this paper	2008
Comparing State-of-the-Art Collaborative Filtering Systems	Candillier, Meyer, and Boulle	A deep dive into the Naive Bayes CF algorithm	2007
Being Accurate is Not Enough: How Accuracy Metrics have hurt Recommender Systems	McNee, Riedl, and Konstan	Paper describing interesting performance metrics apart from accuracy like serendipity and various other similarity metrics	2006
Measuring Surprise in Recommender Systems	Kaminskas and Bridge	Paper describing two ways of measuring surprise in recommender systems	2014
Content-based Recommender Systems: State of the Art and Trends	Lops, Gemmis, and Semeraro	An overview of content-based recommenders in academia and industry and their primary similarities and differences	2010
Auralist: Introducing Serendipity into Music Recommendation	Zhang, Seaghdha, Quercia, and Jambor	Case study on the effect of introducing serendipity, novelty, and diversity into recommendations without sacrificing accuracy	2012
Improving Recommendation Lists Through Topic Diversification	Ziegler, McNee, Konstan, and Lausen	Introduction to intra-list similarity as a metric to assess the topical diversity of recommendations	2005
Latent Dirichlet Allocation	Blei, Ng, and Jordan	Seminal paper on topic modeling and explanation of LDA	2003