Recommenders for Commerce, Content, and Community

Professor John Riedl University of Minnesota

Recommender systems are ubiquitous on the Internet for helping sell products -- everything from automobiles to zebras (stuffed, anyway).Novel applications are emerging that use recommenders for non-Internet applications, and that apply them to the problems of distributing content on the Internet and to developing online communities.
Community-building is proving one of the most successful ways to create "stickiness" among customers. A vibrant community of practice around a company's products creates a powerful barrier to competition, and enables consumers to help sell and support your products.

We will briefly survey Eight Principles of Recommender Systems, illuminated by examples from research and commerce. We will use the Principles to investigate the algorithms that underlie recommender systems, the interfaces for presenting the recommendations, the best practices for deploying them -- and the easiest ways to get a recommender system badly wrong. Along the way we will consider issues of how to build a recommender community from scratch, group recommendations, and consumer privacy. We will conclude with a look at some of the most important active research areas in recommender systems.

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