Build Your Own Personalized Book Recommendation System: Create Your Own Customized Reading List
Content-based recommendation systems are one of the topics in the field of machine learning. For the past few weeks, I’ve been reading and learning about this topic and how it can be applied to build your personalized book recommendations.
Limitation of problem
This article will only focus on the use of content-based recommendation systems
The data required for this project include user-generated content such as book reviews, ratings, and other user-generated content, with the focus being on the title of the book.
Expected Outcome
The expected outcome of this project is a book recommendation system that can accurately recommend books to users based on their preferences. The system should be able to capture user engagement metrics and provide personalized recommendations.
Before Building
what you need to prepare is the dataset itself, The dataset that has been used in this article :
https://www.kaggle.com/datasets/zygmunt/goodbooks-10k
This dataset contains 10,000 books, including title, author, year of publication, ratings and tags from Goodreads.com. With this dataset, you can explore various aspects of the book world such as authors, genres and trends in the publishing industry. Use this dataset to build your book recommender system or analyze the data to discover trends in book publishing. Get creative and see what insights you can uncover.
Because We are gonna use a content-based method, therefore NLP technique is needed
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