Book Recommendation Framework Using Collaborative Filtering with Hybrid User Overlap Modeling

Abstract

This paper introduces a machine learning–based book recommendation system that enhances traditional collab- orative filtering by incorporating a novel hybrid overlapping function. The system processes a dataset of roughly 270,000users,250,000books,and1.2millionratings.Bydiscarding data from users with fewer than 200 ratings and books withfewer than 50 ratings, the dataset was refined to 811 users and 706 books. An 810-dimensional matrix represents user ratingsfor each book, and cosine similarity is used to assess pairwise similarities between books. Additionally, our hybrid overlapping function calculates the ratio of shared user ratings (i.e., the intersection over union) to adjust the similarity scores. Extensive experiments, statistical analyses, and case studies demonstrate that this approach improves recommendation precision by ap- proximately4.7%,reliablyidentifyingthefivemostsimilarbooks to any given title and thereby enhancing user experience.

Introduction

This paper introduces a book recommendation system designed to function effectively in environments with limited data, where metadata or content descriptions are scarce. The system employs a collaborative filtering approach utilizing cosine similarity, enhanced by a novel hybrid overlapping function. This dual method not only measures similarity in an 810-dimensional rating space but also quantifies the actual overlap in user ratings between books. As a result, the system enhances recommendation accuracy and addresses challenges such as data sparsity and the cold start problem.

Key Components:

1. Popularity-Based Filtering: Books are initially ranked based on the total number of ratings received. The top 50 books are presented as popular recommendations.

2. Collaborative Filtering with Cosine Similarity: Each book is represented as a vector in an 810-dimensional space, where each component corresponds to a user's rating. Cosine similarity is computed, considering only overlapping user ratings to ensure that the similarity reflects common user experiences.

3. Hybrid Overlap Function: To overcome limitations of cosine similarity in sparse datasets, an overlap score is calculated as the ratio of users who rated both books to the total number of users who rated at least one book. The final similarity score is the product of the cosine similarity and the overlap score, reducing inflated similarity values due to sparse overlaps.

Implementation:

• Data Preprocessing: Utilizing Python libraries like Pandas, the system removes missing values, duplicates, and filters out users with fewer than 200 ratings and books with fewer than 50 ratings.

• User–Item Matrix Construction: An 810-dimensional matrix is constructed with rows representing books and columns representing users.

• Similarity Computation: Cosine similarity is calculated across the matrix, and the hybrid overlap function is applied to refine the similarity scores.

• Front-End Interface: A dynamic interface features a search bar, displays the top 50 popular books, and presents four recommended books (with cover images) that update live based on user queries.

Conclusion

Thisstudypresentsanenhancedbookrecommendationsys- tem that extends traditional collaborative filtering by integrat- ing a novel hybrid overlapping function. By pre-filtering the dataset to ensure adequate rating density, constructing an 810- dimensionaluser–itemmatrix,andcombiningcosinesimilarity with an overlap metric, our system achieves an approximate 4.7% improvement in recommendation precision. Extensive experiments, statistical analyses, and case studies confirm the robustnessofourapproachinaddressingchallengessuch as data sparsity and the cold start problem. Although the systemperformsexceptionallywellforgenre-specificqueries, further work is required to optimize its performance across broader contexts. Future research will explore richer feature integration, dynamic modeling, and scalable architectures to further enhance recommendation accuracy. Overall, this work contributes a practical, efficient, and adaptable solution for large-scale book recommendation challenges in data-sparse environments.

References

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Copyright

Copyright © 2025 Yatharth Bhalla, Dr. Tejna Khosla. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.