Research on a short video recommendation algorithm based on the Spark platform

With the rapid development of digital technology, short video platforms have quickly risen worldwide, becoming an important part of people's lives. However, this explosive growth has also brought about a significant challenge: how to accurately identify content that interests users from a vast amount of short video content and effectively present it to them. This study focuses on enhancing and optimizing the performance of short video recommendation systems by leveraging effective algorithms, with the aim of ensuring that every user receives video recommendations that highly align with their personalized interests and needs. Additionally, this research tackles the challenging "cold-start" problem, striving to provide new users with a rapid adaptation and accurate recommendation solution.