USING BAYESIAN METHODS TO PREDICT USERS’ INFORMATION NEEDS IN A DIGITAL LIBRARY

In digital library, identifying users’ information needs and providing them with relevant resources is of critical importance. Users' characteristics, personal preferences, and demographic information play a key role in determining their information needs. Machine learning based methods are widely utilized to recommend resources that align with users' information requirements, significantly enhancing the efficiency of information retrieval systems. The Bayesian method is one of the most effective methods for predicting users' information needs. This approach is based on the principle of conditional probabilities and enables the prediction of future information needs by analyzing users' past search behaviors and interaction patterns. The Bayesian model offers several advantages, including the ability to process large datasets, high classification accuracy, and computational efficiency. Due to these benefits, it is extensively applied in digital library to optimize information retrieval and recommend relevant resources to users. This article examines the fundamental principles of the Bayesian method in the context of digital library, highlighting its advantages and limitations. Furthermore, the effectiveness of this model in identifying users' information needs and recommending appropriate resources is analyzed. The findings of this research contribute to the improvement of digital library, enhancing user search experiences and optimizing resource recommendations.