Research On Multi-Domain Recommendation Systems Based on The Thompson Sampling Algorithm

Abstract

In today's information-rich environment, recommendation systems play a pivotal role in helping users efficiently navigate vast amounts of data, with applications spanning online platforms, e-commerce, streaming services, and digital advertising. Among various algorithms, Thompson Sampling (TS) stands out for its ability to balance exploring new options with leveraging known preferences, thereby preventing recommendation stagnation and adapting to shifts in user interests. This paper investigates TS within the context of advertising and movie recommendations, exploring its Bayesian probabilistic foundation, uncertainty modeling, practical integration within recommendation architectures, handling of large-scale data streams, and real-time feedback mechanisms. Performance is evaluated through metrics including accuracy, user engagement, click-through rates, and conversion rates, with comparisons against ETC and UCB algorithms. Results demonstrate that TS enhances recommendation accuracy, user satisfaction, and system revenue, establishing its position as a valuable and versatile tool for improving modern recommendation systems. Furthermore, the study provides insights into practical deployment strategies for real-world applications.