Machine Learning Models for Predicting User Information Needs

Abstract

Meeting user expectations as well as predicting their information requests are primary goals in contemporary information retrieval systems. The advent of machine learning (ML) technologies has brought about new possibilities for anticipating these needs and tailoring systems to provide users with relevant, timely, and context-sensitive information--thereby improving user experience and efficiency. This paper describes the processes involved in building and assessing ML models designed to predict information needs using behavioral, contextual, and content-based signals. Various supervised and unsupervised techniques are considered, including deep learning, decision trees, and probabilistic models, with a focus on the accuracy, scalability, and adaptability of predictions. The incorporation of temporal data, such as query and session behavior histories, is highlighted to boost model performance. Moreover, problems such as data sparsity, privacy of users, and interpretability of the model are discussed. Model effectiveness is tested with real-world datasets from web search engines and recommender systems. The results indicate that the best performance, particularly in rapidly changing settings, lies within hybrid models that integrate both content and collaborative signals. This work extends the emerging field of proactive information retrieval and contributes to the maturation of intelligent systems that monitor user activity and autonomously satisfy their information needs.