This chapter introduces into the recommendation scenario another variable that the previous approaches ignored: context. The specific conditions in which the user expresses a desire, preference, or need have a strong influence on their behavior and expectations. Different techniques exist to consider the user’s context during the recommendation process. We’ll cover the main ones in this chapter.
Furthermore, to complete our overview of recommendation engine models and algorithms, we’ll see how it’s possible to use a hybrid approach that combines the different types of systems presented so far. Such an approach will enable us to create a unique and powerful recommendation ecosystem capable of overcoming all the issues, limitations, and drawbacks of each individual recommendation method.
Suppose that you would like to implement a mobile application that provides recommendations about movies to watch at the cinema; we’ll call it Reco4.me. By using context-aware techniques, you’ll be able to take into account environmental information during the recommendation process, suggesting, for example, movies playing at cinemas close to the user’s current location.