Recommender systems are among the most pervasive machine learning applications on the Internet. Social media, audio and video streaming, news, and e-commerce are all heavily driven by the data-intensive personalization they enable, leveraging information drawn from the behavior of large user bases to offer a myriad of recommendation services. Point of Interest (PoI) recommendation is the task of recommending locations (business, cultural sites, natural areas) for a user to visit. This is a well-established sub-field within recommender systems, and as a domain of application, it provides a good introduction to the challenges of applying personalized recommendation in practical contexts.
An effective PoI recommender must consider a user's interests and preferences, as in any personalized system, but also practical aspects of travel: weather, congestion, hours of operation, seasonality, to name a few. In addition, some PoI recommenders are designed to take multistakeholder aspects of the problem into account. For example, challenges arise in the rivalrous nature of recommending a limited resource across a large user base: a system that recommends the same 50-seat restaurant to 1,000 people on the same day and time will have a lot of disappointed users.