Abstract: The advent of recommender systems has revolutionized the digital landscape, enabling users to effortlessly access personalized web content tailored to their preferences. These systems have become instrumental in streamlining e-commerce experiences, providing users with curated recommendations that align with their tastes. This paper delves into the realm of recommendation systems, with a specific focus on the domain of online book shopping. As the e-commerce landscape evolves, the significance of accurate and efficient recommendations cannot be overstated. Traditional methodologies often fall short, accumulating irrelevant data and impeding the user experience. In response, this paper introduces a novel approach to book recommendations, aimed at enhancing the reader's journey by suggesting the ideal book for their next reading endeavour.
The proposed method centres on User-Based Collaborative Filtering (UBCF), a powerful technique that harnesses the collective preferences of users with similar reading patterns. By leveraging a well-defined set of similarity measures, the system effectively identifies like-minded readers, paving the way for insightful book recommendations. The architecture of the proposed system is meticulously outlined, showcasing its seamless integration into the online book shopping platform. In addition to UBCF, the paper underscores the importance of training, feedback, and data management in bolstering the recommendation process.
The model's implementation is intricately detailed, highlighting its practicality and potential impact. As a user interacts with the system, a symphony of training, analysis, and configuration culminates in the delivery of tailored book recommendations. In conclusion, this paper not only presents a comprehensive overview of recommendation systems in the context of online book shopping but also introduces an innovative approach rooted in User-Based Collaborative Filtering. By bridging the gap between user preferences and available content, the proposed system redefines the book selection process, providing readers with a roadmap to literary exploration that aligns seamlessly with their interests. Through the convergence of cutting-edge technology and intuitive design, the paper offers a promising glimpse into the future of personalized digital experience In an era of information abundance, our approach refines content curation.
Keywords: Recommender system, Collaborative filtering, User-based Book recommendation, Similarity measures, User preferences, Content curation, Data analysis, Information retrieval, Decision-making, Model design, User interaction, Feedback loop, Data management, User engagement.
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DOI:
10.17148/IJARCCE.2023.12735
