Design and Implementation of a Personalized Book Recommendation System Using Machine Learning Techniques
Abstract
The rapid expansion of the World Wide Web has led to a substantial increase in digital information and online commercial activity, resulting in significant information overload for users. In domains such as e-commerce and digital libraries, users often face difficulty identifying relevant content from a large and continuously growing collection of resources. Recommender systems have been widely adopted to address this challenge by assisting users in discovering content that matches their interests through the analysis of user preferences, historical interactions, and collective behavior patterns.
This study focuses on the design and evaluation of personalized recommendation techniques in the book recommendation domain. The work begins with a detailed examination of existing recommender system approaches and user profiling methods to identify their applicability and limitations. User profiles are constructed by analyzing user behavior, including interaction history and rating patterns. Based on these profiles, three recommendation approaches are developed to generate personalized book suggestions.
The proposed system is evaluated using both live user experiments and offline analysis. Standard evaluation metrics are employed to assess the accuracy and effectiveness of the generated recommendations. This combined evaluation strategy ensures reliable performance assessment under practical and controlled conditions.
The results of the evaluation indicate that the recommendation system achieves satisfactory accuracy in predicting user preferences. The findings further show that a hybrid recommendation approach, which combines content-based filtering and collaborative filtering techniques, produces more accurate and relevant recommendations compared to individual methods. The study confirms the effectiveness of hybrid recommender systems for improving personalization and content discovery in the book recommendation domain.
References
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