Recommender Systems I — The What and How

36-462/662, Data Mining, Fall 2019

Lecture 12 (7 October 2019)

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Recommender systems

“You may also like”, “Customers also bought”, feeds in social media, …

Generically, two stages:

Very simple (dumb) baselines

Two immediate baselines, which we’ve seen

Nearest neighbors: content-based

Nearest neighbors: item-based

Factor models

Social recommendations

Exercise: What’s \(\widehat{x_{ik}}\) in terms of the neighbors?

Combining approaches

Some obstacles to all approaches

Missing values are information

Tastes change


Summing up

References (in addition to the background reading on the course homepage)

Marlin, Benjamin, Richard S. Zemel, Sam Roweis, and Malcolm Slaney. 2007. “Collaborative Filtering and the Missing at Random Assumption.” In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence [Uai 2007].

Salganik, Matthew J., Peter S. Dodds, and Duncan J. Watts. 2006. “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market.” Science 311:854–56.

Salganik, Matthew J., and Duncan J. Watts. 2008. “Leading the Herd Astray: An Experimental Study of Self-Fulfilling Prophecies in an Artificial Cultural Market.” Social Psychological Quarterly 71:338–55.