Natural Language Processing for Book Recommendation website
bookclub.ai
bookclub.ai
It uses Transformer and BERT models to understands human level queries, maps its into vector space, find the dot product of story-line embeddings and user queries to generate most relevant books in seconds.
Ask something like ‘Women Lead in World War from Paris’ and it suggests titles like ‘Nightingale’.
It has features like Book-Genie which has used StateOfTheArt algorithms to make sense of human queries from 1Mn+ reddit postings for book suggestions (I recount my waiting times of ~1 Month to get data dumps and training models on top of existing NLP models trained for Question and Answers). Mind-Map feature is another NLP product to provide visualizations of MoreLikeThis books in vector space by understanding story line and good-reads reviews and voting data.
Every book has its dedicated detail page where you get a detailed summary of its storyline, the relevant info about author, ratings, review count and most importantly “More-Like-This” books based on genres, subjects, theme etc. It identifies the lists that can be associated with a given books and performs a ranking using ML based embeddings.
Bookclub.ai is inspired by Goodreads and I see it standing in the same space someday where book-lovers from all over the world can connect and hangout, explore books and share their experiences. Bookclub.ai wants to become the de-facto platform for book lovers, designed to help you find the next great book based on what you have previously read.