Recommender Systems-Beyond Matrix Completion

THE USE OF recommender systems has exploded over the last decade, making personalized recommendations ubiquitous online. Most of the major companies, including Google, Facebook, Twitter, LinkedIn, Netflix, Amazon, Microsoft, Yahoo!, eBay, Pandora, Spotify, and many others use recommender systems (RS) within their services. These systems are used to recommend a whole range of items, including consumer products, movies, songs, friends, news articles, restaurants and various others. Recommender systems constitute a mission-critical technology in several companies. For example, Netflix reports that at least 75% of its downloads and rentals come from their RS, thus making it of strategic importance to the company. In some ways, the systems that produce these recommendations are remarkable. They incorporate a variety of signals about characteristics of the users and items, including people's explicit or implicit evaluations of items. The systems process these signals at a massive scale, often under real-time constraints. Most importantly, the recommendations are of significant quality on average. In empirical tests, people choose the suggested items far more often than they choose suggested items based on unpersonalized benchmark algorithms that are based on overall item popularity.