ABSTRACT
This chapter extends the discussion on recommendation algorithms in the previous chapter, shifting its focus to the customization of recommendation algorithms in a distributed environment. With the rapid development of information technology, information overload has become an important challenge in the Internet field. In order to alleviate the increasing contradiction between Internet users and massive data, researchers have proposed the concept of recommendation systems. As an important branch of recommendation systems, hybrid recommendation systems improve system performance by combining multiple recommendation algorithms and are now widely used in e-commerce, social networks, and video platforms. However, the exponential growth in both user base and data volume has placed heightened demands on the performance of hybrid recommendation systems. Focusing on the deployment and customization of distributed algorithms in distributed environments, this chapter first introduces the background of recommendation algorithms in distributed systems in its first section. Subsequently, it introduces the algorithmic principles of recommendation algorithms. Finally, it introduces specific details of system customization through the lens of a case study involving the deployment of a recommendation algorithm in a distributed environment.
