tyedem/Books-Recommendation-System
Collaborative filtering recommendation system built with sklearn and Amazon books, user and ratings datasets sourced from Kaggle
This project helps e-commerce businesses provide personalized book recommendations to their customers. By analyzing customer reading history and explicit ratings for books, it suggests 10 new books similar to ones they've already enjoyed. This is for online booksellers or anyone managing an extensive book catalog looking to improve customer experience and drive sales.
No commits in the last 6 months.
Use this if you manage an online book store and want to offer tailored book suggestions to your customers based on what they've previously read and rated.
Not ideal if you need a recommendation system that also factors in implicit customer behaviors like browsing history or purchase frequency, as this system relies solely on explicit ratings.
Stars
8
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
Jul 16, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/tyedem/Books-Recommendation-System"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
benfred/implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets
NicolasHug/Surprise
A Python scikit for building and analyzing recommender systems
MengtingWan/goodreads
code samples for the goodreads datasets
GHamrouni/Recommender
A C library for product recommendations/suggestions using collaborative filtering (CF)
jaimevalero/github-recommendation-engine
A github repository suggestion system