Collaborative Filtering Recommender Systems


Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. 2011. Collaborative Filtering Recommender Systems. Foundations and Trends® in Human-Computer Interaction 4(2) (February 1st, 2011), 81–173. DOI 10.1561/1100000009. Cited 1652 times. Cited 644 times.


Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance. Research in the field is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains. The differing personalities exhibited by different recommender algorithms show that recommendation is not a one-size-fits-all problem. Specific tasks, information needs, and item domains represent unique problems for recommenders, and design and evaluation of recommenders needs to be done based on the user tasks to be supported. Effective deployments must begin with careful analysis of prospective users and their goals. Based on this analysis, system designers have a host of options for the choice of algorithm and for its embedding in the surrounding user experience. This paper discusses a wide variety of the choices available and their implications, aiming to provide both practicioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.



  • On p. 95, section 2.2.3: the value of rD,er_{D,e} should be 3, not 2, making the final score 4.285.
  • On p. 123, figure 3.2(b): the formula for Recall should be TPTP+FN\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}. The diagram is correct.
  • On p. 124, figure 3.3(b): the formula for Specificity should be TNTN+FP\frac{\mathrm{TN}}{\mathrm{TN} + \mathrm{FP}}. The diagram is correct.

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