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"Clickstream-Based Intelligent Recommender System" is an insightful book authored by Shalini Gupta. This book delves into the fascinating field of recommender systems and explores the utilization of clickstream sessions to enhance the effectiveness and intelligence of these systems. In the modern digital landscape, the availability of vast amounts of user-generated data presents unique opportunities for personalized recommendations. Shalini Gupta, with her expertise in the field, offers a comprehensive exploration of the use of clickstream data, which captures users' interactions and behavior while navigating websites or applications. By analyzing clickstream sessions, valuable insights can be gained into user preferences, interests, and patterns. The book covers various aspects of developing intelligent recommender systems, including data collection and preprocessing techniques, feature extraction, and machine learning algorithms. It delves into advanced methods such as collaborative filtering, content-based filtering, and hybrid approaches that combine multiple techniques to provide accurate and personalized recommendations. Shalini Gupta emphasizes the importance of understanding user context and the dynamic nature of user preferences. The book explores techniques for adapting to changes in user behavior and continuously updating recommendations based on real-time data. With its practical approach, the book also addresses challenges related to scalability, efficiency, and privacy in recommender systems. It provides valuable insights and guidelines for researchers, practitioners, and students interested in leveraging clickstream data to build intelligent and effective recommender systems. "Clickstream-Based Intelligent Recommender System" is a valuable resource that offers a comprehensive understanding of the field and serves as a guide for designing and developing intelligent recommender systems that cater to the unique preferences and needs of individual users.