Gathering dataset from Goodreads website

scrape data from www.goodreads.com and store dataset in JSON file

The primary goal of this project is to develop a collaborative book recommendation model using goodreads dataset that can predic user ratings for different books and suggest readers what books to read next. EDA and interactive visualizations are utilized to understand users reading preferences and factors that lead to a book's or author's success in the market.

This project associated with my university for milestone project. A book recommender system using k-means clustering with content based approach from goodreads book dataset.

EDA and recsys models on Goodreads book dataset.

Python Telegram bot for book recommendations using collaborative filtering and Goodreads dataset with a Tinder-style swiping interface

Book Hive is a personalized book recommendation system using Go for the backend and Astro for the frontend. Users can explore books by genre and receive tailored recommendations based on their preferences, powered by a dataset of books from Goodreads..

Using the Goodreads dataset we are going to use Python to train a model that predicts a book’s rating. It should include exploratory analysis of the data, feature engineering and selection, model training and evaluation.

Creates a dataset for book ratings by scraping Goodreads

Book recommender system in command line using Kaggle GoodReads dataset

A content based book recommendation app from 7k-goodreads-book dataset using cosine similarity

This is a book recommendation system based on the book rating data from GoodReads_100k dataset. The dataset contains 100k book.

Recommender Engine for the Books on GoodReads Dataset: deeper insight at data-driven book recommendations

Book recommendation system using rank-based, user-based, and SVD collaborative filtering on the Goodreads dataset.

Data analysis and visualization of Goodreads dataset using Tableau. Includes insights on book ratings, authors, and publishers

Scraping books data from goodreads.com website.best book of the year for my thesis testing dataset.

Implement Boolean Model on search engine to assist users in finding book titles through synopsis and authors using datasets from GoodReads web crawling results.

This project works with the GoodReads Book-Review Dataset to find all pairs of books that a given user reviews for all users within the dataset. These pairs and their frequencies are computed using pySpark and are used to create a tsv file with book titles using SparkSQL.

NextChapter is a personalized book recommendation engine. It takes user preferences—such as mood, favorite genre, and whether they want something similar or different—and suggests a curated list of books from a dataset of over 100,000 GoodReads entries.

In this notebook, we're working with a difficult dataset that includes reviews from the Goodreads book review website as well as a variety of attributes that describe the items. You must also guess the review rating, which ranges from 0 to 5.

This repository classifies Goodreads Fantasy book reviews into subgenres using advanced topic modeling techniques like NMF, LDA, and BERTopic. A dataset of 2M English-language reviews is analyzed, with topics compared to predefined subgenres using cosine similarity. Heatmaps and summaries visualize the results.

Goodreads Book Dataset

Recommendation of book(Goodreads dataset)

Explore book data from Goodreads via Bright Data API for market research and analysis.

Bright Data API를 통해 Goodreads의 도서 데이터를 탐색하여 시장 조사 및 분석에 활용하세요.

Erkunden Sie Buchdaten von Goodreads über die Bright Data API für Marktforschung und Analyse.

Bright Data API を使用して Goodreads の書籍データを探索し、市場調査と分析を行います。

Book Recommendation System for Goodreads Book dataset.

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