Markov chain, information retrieval, personalized recommendation
Abstract
This paper introduces the fundamental role of Markov chain algorithm in recommendation system, especially its “memory-free” property, that is the future state only depends on the current state and has nothing to do with historical data. This feature is useful for simulating user behavior on the platform. The researchers defined the concept of “status” and selected key metrics based on how users interact with search results (like likes, favorites, time spent watching videos, etc.). These indicators are crucial for building predictive models. The paper also discusses the application of Markov chains in recommendation systems, including how transfer probabilities are used to guide information retrieval behavior and population retrieval history is used to weigh these probabilities. In addition, the potential of combining Markov chains with other algorithms and models is also explored. Finally, the abstract emphasizes the importance of considering user data and privacy ethics when developing software and concludes that Markov chain algorithms are an important foundation of recommendation systems, capable of predicting user preferences and enhancing user experience. The paper will show how to use Markov chains to make more accurate predictions.
