• Shradha D. Choudhari
• Dr. Padmapani P. Tribhuvan

Nowadays recommendation system play very important role in enhancing user experience by providing them the relevant items. The book recommendation system help user by suggesting the book as they want. This paper explores the item-based recommendation system and methodologies that used for recommendation system by calculating similarity between items. K-nearest neighbor and single valued decomposition are both machine learning algorithms which have unique algorithms and used to enhance accuracy and efficiency of the recommendation system. K-nearest neighbor finds item based on the user ratings while single valued decomposition is the matrix factorization approach which reduce dimensionality of data and capture latent factors which improves the accuracy and efficiency of the recommendation system. This paper focuses on existing research on this technique.

cosine similarity, item-based collaborative filtering algorithm, k-nearest neighbor, singular value decomposition. Introducti

"A Review of Book Recommendation Systems using Item-Based Collaborative Filtering Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.j607-j611, June-2024, Available :http://www.jetir.org/papers/JETIR2406964.pdf

"A Review of Book Recommendation Systems using Item-Based Collaborative Filtering Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppj607-j611, June-2024, Available at : http://www.jetir.org/papers/JETIR2406964.pdf