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Global Book Insights Dataset
Education & Learning Analytics
Tags and Keywords
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"No reviews yet"
Free
About
This dataset was created to help users discover new books they might enjoy based on titles they have previously read. It features 10,000 of the most recommended books across various genres. The data can also be invaluable for projects involving cross-content analysis and recommendation systems, potentially integrating with film or television show datasets.
Columns
- Book: The title of the book. This sometimes includes details about the series it belongs to within parentheses, which can be extracted for analysis focused on series.
- Author: The name of the book's author.
- Description: The summary or description of the book as presented on Goodreads.
- Genres: Multiple genres assigned to the book, as classified on Goodreads. This is useful for multi-label classification or content-based recommendations.
- Average Rating: The average rating for the book, on a scale of 0 to 5, as given on Goodreads.
- Number of Ratings: The total count of users who have provided ratings for the book. This should not be confused with the number of reviews.
- URL: The direct Goodreads URL leading to the book's specific details page.
Distribution
This dataset comprises 10,000 book entries. It is typically provided in a structured file format, such as CSV. The data reveals a high number of unique values for book titles (9,871), authors (6,064), and descriptions (9,889). While 10% of entries may list 'Fiction' as their sole genre, the remaining 90% feature a diverse range of other genre classifications. Average ratings predominantly fall between 3.50 and 4.50, accounting for over 8,900 books. The number of ratings for individual books varies considerably, with a significant majority (9,836 books) receiving up to 927,813 ratings. The dataset is freely available for use.
Usage
This dataset is well-suited for several applications:
- Clustering: Grouping books or authors based on their descriptions and assigned genres.
- Content-Based Recommendation Systems: Building systems that suggest books using a combination of genres, descriptions, and user ratings.
- Genre Prediction: Developing models to predict book genres from description text, which is an example of multi-label classification.
- Cross-Content Analysis: Utilising this dataset alongside others, such as IMDb datasets with descriptions, for a variety of analytical scenarios.
Coverage
The dataset includes 10,000 highly recommended books across a multitude of genres. The information was gathered from Goodreads, a globally accessible platform. The collection encompasses books considered "of all time," without specific time-range limitations or narrow demographic targeting.
License
CCO
Who Can Use It
- Data Scientists and Machine Learning Engineers: Ideal for training models for recommendation engines, multi-label classification, and natural language processing tasks.
- Researchers: Suitable for academic studies on literary trends, authorial styles, and genre categorisation.
- Application Developers: Can integrate book recommendation features into new or existing applications.
- Students and Educators: Provides a practical resource for learning about data analysis, machine learning project development, and content-based filtering.
Dataset Name Suggestions
- Curated Books 10K Multi-Genre
- Goodreads Top Recommendations
- Essential Reading List Data
- Global Book Insights Dataset
- Book Recommendation Data Hub
Attributes
Original Data Source: Best Books (10k) Multi-Genre Data
QUALITY