Should I Build It Should I Build It
02 Jul 2025
Artificial Intelligence Books

ai book recommendation website that recommends books based on your ...

...thoughts

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Run Away

Multiple attempts have failed with clear negative feedback. Continuing down this path would likely waste your time and resources when better opportunities exist elsewhere.

Should You Build It?

Don't build it.

Your are here

You're venturing into a crowded space: AI-powered book recommendations. Our analysis reveals a significant number of similar products already exist (n_matches=21), which means you will face substantial competition. The average engagement for these products, indicated by the number of comments, is moderate. Unfortunately, we don't have any data to show whether people will actually use or buy this product, but there is plenty of user feedback available from similar products. The 'Run Away' category suggests caution, as many previous attempts haven't succeeded and received negative feedback. It's crucial to understand why these products failed before investing further time and resources. Building a successful AI book recommendation engine will be an uphill battle.

Recommendations

  1. Carefully analyze the criticism from similar products. Users have complained about generic recommendations, lack of personalization, narrow book selections, and technical issues such as errors. Understand these pain points to identify opportunities for differentiation.
  2. Instead of building a website, consider pivoting to solve a more specific problem within the book discovery space. Could your AI skills be applied to help authors find beta readers, or to analyze reader reviews for publishers? Focus on a niche where existing solutions are lacking.
  3. If you've already started developing your AI, explore whether the underlying technology could be repurposed. Could your AI be used for sentiment analysis of book reviews, or for generating personalized book summaries?
  4. Talk to people who have used existing AI book recommendation services like "What to Read After?" to understand their unmet needs. Ask them about their frustrations with current recommendations and what they truly desire in a book discovery experience.
  5. Before committing to a specific direction, conduct thorough market research to identify a unique value proposition. What specific niche or user need can you address that existing solutions are failing to satisfy? Consider underserved genres, reading preferences, or user demographics.
  6. Focus on UX early. Many users complain about poor UX, so think about your site's design, search functionality, and user experience. Address errors as fast as possible.

Questions

  1. Given the numerous existing AI book recommendation tools and the criticisms they've received, what is your unique angle that will make your product stand out and offer genuine value to users?
  2. How will you address the common concerns about lack of personalization, biases towards popular books, and the need for a more diverse selection of titles, as highlighted in the feedback from similar products?
  3. How can you balance the benefits of AI-powered recommendations with the desire for human curation and discovery, given the criticisms surrounding algorithmic bias and the preference for personal recommendations?

Your are here

You're venturing into a crowded space: AI-powered book recommendations. Our analysis reveals a significant number of similar products already exist (n_matches=21), which means you will face substantial competition. The average engagement for these products, indicated by the number of comments, is moderate. Unfortunately, we don't have any data to show whether people will actually use or buy this product, but there is plenty of user feedback available from similar products. The 'Run Away' category suggests caution, as many previous attempts haven't succeeded and received negative feedback. It's crucial to understand why these products failed before investing further time and resources. Building a successful AI book recommendation engine will be an uphill battle.

Recommendations

  1. Carefully analyze the criticism from similar products. Users have complained about generic recommendations, lack of personalization, narrow book selections, and technical issues such as errors. Understand these pain points to identify opportunities for differentiation.
  2. Instead of building a website, consider pivoting to solve a more specific problem within the book discovery space. Could your AI skills be applied to help authors find beta readers, or to analyze reader reviews for publishers? Focus on a niche where existing solutions are lacking.
  3. If you've already started developing your AI, explore whether the underlying technology could be repurposed. Could your AI be used for sentiment analysis of book reviews, or for generating personalized book summaries?
  4. Talk to people who have used existing AI book recommendation services like "What to Read After?" to understand their unmet needs. Ask them about their frustrations with current recommendations and what they truly desire in a book discovery experience.
  5. Before committing to a specific direction, conduct thorough market research to identify a unique value proposition. What specific niche or user need can you address that existing solutions are failing to satisfy? Consider underserved genres, reading preferences, or user demographics.
  6. Focus on UX early. Many users complain about poor UX, so think about your site's design, search functionality, and user experience. Address errors as fast as possible.

Questions

  1. Given the numerous existing AI book recommendation tools and the criticisms they've received, what is your unique angle that will make your product stand out and offer genuine value to users?
  2. How will you address the common concerns about lack of personalization, biases towards popular books, and the need for a more diverse selection of titles, as highlighted in the feedback from similar products?
  3. How can you balance the benefits of AI-powered recommendations with the desire for human curation and discovery, given the criticisms surrounding algorithmic bias and the preference for personal recommendations?

  • Confidence: High
    • Number of similar products: 21
  • Engagement: Medium
    • Average number of comments: 8
  • Net use signal: -8.1%
    • Positive use signal: 10.1%
    • Negative use signal: 18.2%
  • Net buy signal: -8.6%
    • Positive buy signal: 0.6%
    • Negative buy signal: 9.1%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

Similar products

Relevance

What to read after? Personalised AI book recommendations

07 Oct 2023 Artificial Intelligence Books

Personalised AI book recommendations with WOW effect! Greetings! I'm Robo Ratel, your personal AI librarian, and I'm thrilled to help you find your next amazing literary adventure! Just specify genres and/or storyline characteristic and see the exciting recommendations I've curated just for you!

Users criticized the product for lacking a 'what to read after' feature, misleading title and name, and poor UX. There were technical issues like AI Librarian API errors and adblocker interference. Recommendations weren't personalized, with overfitting and limited genre selection. The term 'chick lit' was seen as sexist. Users wanted Goodreads integration, a larger catalog, and multiple story attribute selection. Privacy concerns were raised, and the site was called a mess. Login was restricted to Google accounts.

Avatar
31
20
-25.0%
-15.0%
20
31
Relevance

AI Bookstore - Find the book you want to read with the help of AI

04 Apr 2023 Entertainment Artificial Intelligence Books

AI Bookstore - Personalized Book Recommendations Discover AI Bookstore, a web service using ChatGPT API to recommend books based on your unique preferences.

The launch received positive feedback, with users praising the product and its recommendations. Some users expressed reservations about it replacing a book recommendation feature. There's also a suggestion to list the project in GPT Feed.

The primary criticism is concern that the product might replace personal book recommendations, suggesting a fear of automation or a perceived lack of personalization compared to human interaction.

Avatar
89
4
4
89
Relevance

Librarian AI – Find your next favorite book with AI

10 Apr 2023 Artificial Intelligence Books

Users find the results superficial and note that the tool confuses fiction and non-fiction. Additionally, they believe GPT-4 provides better recommendations.

The product received criticism for providing superficial results and being unable to distinguish between fiction and non-fiction. Additionally, it was described as not good overall.

Avatar
2
2
-100.0%
-50.0%
2
2
Relevance

BookSurfAI - AI-Powered Book Recommendations

26 Jul 2024 Artificial Intelligence Education Books

Use the power of AI to choose your next book! BookSurfAI analyzes your preferences and recommends books you’ll love! Don’t waste hours searching and choosing your next book. With BookSurfAI, you're just seconds away from finding your favorite book.

Excited about AI book recommendations; wants user reviews/ratings.

Avatar
10
1
100.0%
1
10
100.0%
Relevance

Book Findr AI - Your personalized book recommender

24 Jan 2025 Tech

Discover the perfect book with Book Findr AI, your AI-powered book recommender. Get tailored recommendations based on your reading preferences. Find your next favorite book using our advanced AI technology for personalized book discovery.

Avatar
2
2
Relevance

Personalized book recommendations with Librarian AI

12 Apr 2023 Artificial Intelligence Books

Users appreciate book recommendations but desire more personalized and varied suggestions, with some expressing interest in deeper understanding and feedback mechanisms. There's a call for integrating Goodreads and LibraryThing for richer data, while others note biases towards popular books and sequels. The recommendation algorithm and its transparency are questioned, with mixed reviews on its effectiveness. Some users suggest improvements like a thumbs up/down feature and the ability to input a list of read books. The site's design and search functionality receive praise, but there are requests for more non-obvious, unique recommendations. Technical issues and errors are also mentioned.

Users criticize the product for generic and repetitive recommendations, lack of personalization, and a narrow selection of books. The recommendation engine is seen as unsophisticated, with a bias towards popular books and a failure to consider user preferences like language settings. The search function and book database need improvement, with many books missing. Users also note a lack of transparency and explanation regarding the algorithm and data usage, and the absence of features like Goodreads import and feedback options. Some users experienced technical issues like error messages.

https://news.ycombinator.com/user?id=rpastuszak

Have to considered using referral links for some of the recommended books?I don't think that everything needs to be monetised. On the contrary - there's too much stuff existing merely to just extract value from users. But, the recommendations I've seen here look really good and I'm already ordering one of the books (the other one I've already bought).

https://news.ycombinator.com/user?id=ethanbond

Just used this successfully to find a book about olfaction, which I've been looking for and asking around for for a while! Thanks for this service :)

https://news.ycombinator.com/user?id=pchangr

Wow! I don’t know why I had actually never asked a librarian for recommendations.. seems so obvious now. I’ll do next time ^^

https://news.ycombinator.com/user?id=bwb

Thanks, this is super helpful! I love Bill Bryson as well :)Yep, I totally understand what you are saying, and that is what I am working toward. It is a challenge.Right now, I ask authors to share 5 books they love around a topic/theme/mood. So I am collecting a human grouping of books similar in some way that humans decided along with why they love it.For example, if you like "Notes from a Small Island" or "Down Under" (two of my fav Bill Bryson books), here are books that authors have grouped with it and also loved:https://shepherd.com/books-like/notes-from-a-small-island https://shepherd.com/books-like/down-underSo I am using human groupings to help discover books that humans associate with his work somehow. And above each book, their list title helps you understand how their mind grouped it.For example, "Into The Wild" is associated with it based around a human grouping books that capture the spirit of Jack Kerouac's On The Road.What do you think?What is next to improve this?I'll add filters to help people hone in on why they loved the book to help pull that thread out and show books along those lines. For example, if I liked Catch 22 for its absurd humor, I want to make that the common denominator on the page. Or if I like "From a Small Island" for the humor and history, picking those to try to find books with a similar book DNA.In about 4 to 5 weeks I'll ship the first version of this on the bookshelf pages. And in maybe 12 to 16 weeks on the books-like pages, as that needs a lot more work.And, I am hoping next year to launch Book DNA, where I pull in reader data and help line up books that are your all time favorites with others to map this out a bit further. Slowly getting there and a lot will dependon collecting more data from readers in 2024.

https://news.ycombinator.com/user?id=acjacobson

Nice! These "books-like" lists feel and resonate better with me. From the first list I've read Notes from a Small Island, Into the Wild, and Wild, and now some of the others on the list that I don't know, like Llama Drama, look promising. I think you're on the right track.I like the idea of pulling as many threads out of a book to drill into why you might like something. Especially if those threads are not necessarily related to each other as that should widen the recommendation pool.I still think there is something worth doing with author links as well. I played around with gnooks.com after someone else posted a link and within a few minutes found authors I had never heard of that had books that looked interesting.

https://news.ycombinator.com/user?id=themodelplumber

I just tried it. The look and feel is really nice. The topical search was helpful. Bookshelves are a cool idea and "lists we think you will like" in the footer was helpful when I found a list that was OK but with books that were too academic for my interests in a topic.Book Title search seemed like it could use some kind of "not found, but..." Helper, or suggestion functionality.I searched "Moscow Calling," "Remembered Prisoners of a Forgotten War," and "Nixon at the Movies" with no title results. Huckleberry Finn worked OK.Would use it again though, thanks for dropping a link in the discussion.

https://news.ycombinator.com/user?id=yieldcrv

Exactly, I want a new echo chamberI can import the same echo chamber anywhere I goI want to hop dimensions, we dont have a shared reality and are coddled with a familiar soma just like in brave new worldHonestly the best I’ve seen for this are website that just let you browse YouTube and social media sites under a different users session

https://news.ycombinator.com/user?id=johnfn

GPT4 is pretty good at this. Here's what I got on a first run, though you can keep prompting it to go more esoteric if you want. I honestly think that GPT4 will obsolete most existing recommendation systems; it's already better than what I previously used (Goodreads).> Prompt: give me some books similar to "Three Body Problem", "Brave New World", and "Dune" - but dont give me the common suggestions, give me some more esoteric stuff i probably haven't heard of> I understand that you're looking for lesser-known, yet thought-provoking science fiction novels similar to "Three Body Problem," "Brave New World," and "Dune." Here are some esoteric suggestions that you might not have heard of:> "Blindsight" by Peter Watts - This hard sci-fi novel explores the nature of consciousness and humanity's first contact with a terrifyingly alien intelligence.> "The Quantum Thief" by Hannu Rajaniemi - A post-human heist story set in a future Solar System, where advanced technology has given birth to an array of strange and wondrous societies.> "The Left Hand of Darkness" by Ursula K. Le Guin - A groundbreaking work of feminist science fiction that explores gender and societal norms on a planet where inhabitants can change their sex at will.> "The Stars My Destination" by Alfred Bester - A classic sci-fi novel that combines elements of revenge, teleportation, and corporate intrigue in a richly imagined future world.> "The Dispossessed" by Ursula K. Le Guin - A thought-provoking tale of two societies, one anarchistic and the other capitalistic, and the physicist who attempts to bridge the divide between them.

https://news.ycombinator.com/user?id=flir

I really think it's the depth, not the breadth, that makes the recommendations work. I've been a member since 2005, and SciFi was pretty good right out of the gate. Now, everything's pretty good. If it hasn't been harmed by its growth up to this point, I honestly doubt a little more will hurt it.

https://news.ycombinator.com/user?id=summarity

If you want to compare books, I've got a project for that: https://findsight.ai I use it for my learning as a PM to skip over books that just copy & paste from each other.A related project is https://shepherd.com - but it focuses on high quality human suggestions.

https://news.ycombinator.com/user?id=FredPret

Love this. I put my three favourite books, and got out two recommendations that I already read and loved, and two ones that appear to be likely new favourites

https://news.ycombinator.com/user?id=TopTheThaiGuy

The recommendations were great! I have read all except one. Which mean that it really knows what kind of book I read as well. Can I ask if you use GPT api for this? or you used your own model. I'm also interested in the model where it can summarize book, index it and be able to fetch it later.Thank you!

https://news.ycombinator.com/user?id=themodelplumber

I got some interesting results from this that I wasn't expecting. I like that it found every title I searched.Though the auto-complete kind of went off my phone screen, I knew I should scroll down to check.Thanks for sharing it, will use again for sure.

https://news.ycombinator.com/user?id=agon123

Thank you for making this!I just found the botany of desire - next book I think.

Avatar
150
103
-2.9%
-2.9%
103
150
13.6%
1.0%
Relevance

Nextread.ai – AI-powered book recommendations with a twist

21 Oct 2024 Artificial Intelligence Books

Users report frequent errors when trying to use the service, particularly with book recommendations. There are complaints about a small selection of books, poor quality recommendations, and issues with the website's functionality, including broken links and language problems. Some users find the animations enjoyable but criticize the backend and the AI-generated content. A few comments appreciate the front-end design but suggest improvements for affiliate marketing. Overall, there is a sentiment of disappointment with the service's current state, with some users expressing sarcasm about its usefulness.

Users report frequent errors, including a 500 error and issues with the site being down or buggy. The AI's recommendations are criticized for being poor, redundant, or irrelevant, especially for niche books. The interface requires polishing, and the back-end seems to be malfunctioning. Users also note problems with the blog section, such as broken links and low-quality AI-generated content. There are complaints about the necessity to sign in and language issues in the displayed results.

Avatar
10
18
-38.9%
-38.9%
18
10
Relevance

Bookshlf - Your AI-powered book recommender to learn anything

20 Nov 2023 Artificial Intelligence Education Books

Why waste time searching when you have this AI-powered Book Recommender? Share your learning interests and embark on a learning journey filled with the finest books recommended just for you.

The Product Hunt launch received positive feedback, with users congratulating the team and expressing excitement about the product's potential for simplifying book discovery. Several users highlighted the pain point of finding relevant learning books and praised the tool's ability to address this need. One user inquired about the chatbot's book quality assessment, while another suggested a deeper understanding of user interests for improved recommendations. Overall, the tool was well-received, with users finding it potentially helpful and visually appealing.

The primary criticism suggests a need for deeper understanding of the reader's criteria to improve the product. Further details are not provided in this single point of criticism.

Avatar
81
10
20.0%
10
81
20.0%
Relevance

WhatToReadAfter- AI Book suggestions with wow effect

03 Nov 2023 Artificial Intelligence Books

If you, like me, often wonder what to pick up next after a captivating read, then I've found the perfect solution for you! Check out WhatToReadAfter, a platform that lets you swiftly rate books you've finished and suggests new ones based on both AI-powered recommendations and insights from other readers.

Recommends WhatToReadAfter for book suggestions.

Avatar
3
1
100.0%
1
3
100.0%
Relevance

Decide your next read from any booklist basis your Goodreads history

06 Aug 2024 Artificial Intelligence Books

Hello all! I am a fintech product manager inspired by this community to build things.The motivation to build this came while going through NYT's List 100 Best Books of the 21st Century. I wanted to see which one I could pick as my next read. I realised that I couldn't get through the descriptions of the 100 books, then go through the process of searching Goodreads ratings for each, read people's comments, evaluate and so on. Could there be something faster which would give me an idea of which books I would like?So I built a MVP personalized book recommender which takes all your Goodreads reading history (thanks to Goodreads export option), creates a model (neural network) and then gives predicted ratings for any new book i.e. a prediction of how much would you rate it from 1 to 5 stars on Goodreads. This is especially useful if you are trying to decide which books to read from a long list of books. The model will give predicted ratings against each book and you can then choose if you would like to read something which has a high predicted rating or push yourselves towards trying something new.Since this builds a neural network-based predictive model right on the fly, it takes time to build if your reading history is long. I am trying to see how I can improve on that.I have just added 2 list of books against which you can get predicted scores - Obama's recommendations and Pulitzer list of books. Would love to hear from you if you want me to add another set of books. A feature in the works is extracting all books from a webpage against which you can get predicted book ratings. For ex: On book discussion pages on Hackernews, there are many books mentioned by community members. This model can help you decide which one to read next in a few moments!How it works? I am fetching book descriptions basis the title and author of book from Google Books API and then embedding the same using GPT-4. Then building a neural network with embeddings of each books descriptions and the ratings I have given in my reading history. I have used Pytorch to build the same.Would love to hear suggestions, comments, ideas, critiques!You can choose to reach out to me at: shubham13596@gmail.com or https://www.linkedin.com/in/shubham-gupta-50267a90/Thanks!

Prefers independent bookstores, dislikes Goodreads and algorithms.

Dislikes Goodreads, Amazon, and manipulative algorithms.

Avatar
2
1
-100.0%
-100.0%
1
2
Relevance

Hacker News Book Recommendations

19 Oct 2024 Artificial Intelligence Books

After reading this Ask HN post [1] (and buying too many books), I found myself going through old posts about book recommendations. I wanted to find all books recommended in those posts and save the top ones for future book shopping sprees. This has been done before [2, 3], but I wanted to get books that were recommended, not just mentioned.I used the Algolia API [4] to get all Ask HN posts about books, reviewed them and filtered out ones that were too specific e.g best books to learn math. The comments were then sent to the Open AI API [5] to extract books that are recommended.I then grouped them, but books can be mentioned with different names (Godel, Gödel, GEB?), and GPT4 wasn't doing a good job finding similar titles in a long list (25k titles). So that part had to be done mostly manually.Finally, I added a page to browse by posts, some stats (like users who recommended the most books), and highlighted mentioned books in the comments.I hope you find a good book! Feedback is welcome :)[1] https://news.ycombinator.com/item?id=41756432 [2] https://hacker-recommended-books.vercel.app [3] https://ramiro.org/vis/hn-most-linked-books/ [4] https://hn.algolia.com/api [5] https://platform.openai.com/docs/api-reference/introduction

Avatar
8
8
Top