AUTOMATIC PERSONALIZED BOOK RECOMMENDATION SYSTEM USING DEEP LEARNING METHODS

AUTOMATIC PERSONALIZED BOOK RECOMMENDATION SYSTEM USING DEEP LEARNING METHODS

K. S. P. Abhiram Chowdary

K. Shravani

K. Akshay Kumar

K. Athreya Sai Gopal Acharya

V. Sravanthi

Assistant Professor

Artificial Intelligence & Machine Learning

Dr. Thayyaba Khatoon

Professor

Artificial Intelligence & Machine Learning

Malla Reddy University, Hyderabad

ABSTRACT:

This paper presents a deep learning project that addresses the difficulty for users to find relevant and personalized book recommendations in the vast and diverse landscape of available books. We develop a novel deep learning model hybrid architecture that combines the content-based filtering approach and the collaborative filtering approach, and evaluate its performance on the “Goodreads Dataset” which consists of historical user-book interactions, including ratings, reviews, and other relevant user behaviours and also contains detailed information about the books, such as genre, author, publication year, and textual descriptions. The evaluation metrics used include precision, recall, and F1 score, which measure the accuracy and relevance of the recommended books. Our results demonstrate that our approach outperforms existing methods in the literature and has potential for future applications in digital libraries, online reading communities, E-commerce platforms, personal digital assistants, social media and mobile applications. This paper contributes to the field of artificial intelligence and machine learning, specifically in the area of recommender systems for book recommendations by improving recommendation accuracy and performance, enhancing personalization, addressing limitations like the cold-start problem, demonstrating practical applicability and showcasing practical relevance, and advancing the field of recommendation systems as a whole through the introduction of a novel hybrid model for book recommendations and providing insights into hybrid approaches.