1 point by bookworm 5 months ago flag hide 32 comments
bookworm123 5 months ago next
This is really cool! I would love to see how well it can recommend books based on what I've read in the past.
bookworm123 5 months ago next
@neural_knower I've read a lot of sci-fi and fantasy books in the past. Can you recommend some within those genres?
neural_knower 5 months ago next
@bookworm123 Sure thing! Here are some top sci-fi and fantasy recommendations based on our model: <br> - Sci-fi: 'The Martian' by Andy Weir, 'Dune' by Frank Herbert <br> - Fantasy: 'The Name of the Wind' by Patrick Rothfuss, 'The Final Empire' by Brandon Sanderson
bookworm123 5 months ago next
@neural_knower Thanks so much for the recommendations! I'm sure I'll enjoy them.
machinelearnr 5 months ago prev next
I'm really interested in the use of neural autoencoders here. Can you share more about how you implemented them?
neural_knower 5 months ago next
@machinelearnr I used a standard feedforward neural network Autoencoder, trained with a mean squared error loss function. I also used a dense layer of 64 units.
datascience_fan 5 months ago next
@neural_knower That's very interesting, thanks for sharing! <br> How do you ensure that the recommendations are diverse and not just similar books within the same genre?
neural_knower 5 months ago next
@datascience_fan Great ques...*continues on next JSON object*
neural_knower 5 months ago next
...tion. To ensure diversity in our recommendations, we have a balancing term in the loss function that penalizes selecting similar books. Additionally, we use context information such as users' preferences for authors, settings, and writing styles to help diversify the recommendations.
deeplearner 5 months ago next
@neural_knower That's an interesting approach! Do you think this technique can be applied to other domains like movie or music recommenders?
machinelearnr 5 months ago next
@deeplearner Yes, it could be applied to other domains as a general framework, with the loss function and feature engineering adjusted to suit.””
datascience_fan 5 months ago next
@machinelearnr Additionally, you might consider including
deeplearner 5 months ago next
@datascience_fan Yes, we want to include dimensionality reduction techniques like PCA for feature scaling and improved performance since it's a fairly large feature set with sparsity.””
datascience_fan 5 months ago prev next
I've been looking for a good book recommendation system and this one looks very promising. Good job!
machinelearnr 5 months ago next
@datascience_fan I agree, it's very promising! One thing I'm curious about is how it handles new users who don't have any reading history.
neural_knower 5 months ago next
@machinelearnr For new users, we ask them to fill out a short survey about their preferences and reading habits. From there, we can make educated recommendations based on that information.
learner 5 months ago prev next
I'd like to try this out! Can you share any details about the deployment of your system?
learner 5 months ago next
@learner Of course! I deployed it as a web app using Flask and SQLite for the database.
bookworm123 5 months ago next
@learner It looks great! Can't wait to try it out.
datascience_fan 5 months ago next
@bookworm123 You're welcome! I can't wait to read your review of the recommendations.
neural_knower 5 months ago next
@datascience_fan We use a specific accuracy metric for this project - we look at the intersection of the recommended books for a user and the user's reading history. The accuracy metric is calculated as the ratio of the number of correct recommendations to the total number of recommendations made.
quant 5 months ago next
@neural_knower I see, it's a relative accuracy metric. Do you have plans to compare against other recommendation systems?
learner 5 months ago next
@quant Yes, we plan to compare our system with other popular recommendation systems and are currently working on setting up those experiment.””
deeplearner 5 months ago prev next
Very cool project! I'd be interested in contributing to the project. Is it open-source?
learner 5 months ago next
@deeplearner It's not open-source yet, but once it's ready for production we plan to release it as an open-source project.
quant 5 months ago next
@learner That's great to hear! I look forward to contributing to the project when it's open-sourced.
quant 5 months ago prev next
I'm interested in the evaluation of your system's performance. How do you measure accuracy and diversity?
learner 5 months ago next
@quant To evaluate the system's performance, we measure accuracy by comparing the recommended books to users' reading histories and rating them based on how many were actually read. For diversity, we calculate the entropy of the genre distribution of recommendations to ensure it's well-balanced.
deeplearner 5 months ago next
@learner Interesting, it sounds like the evaluation process is well thought-out. Best of luck releasing it as open-source!
neural_knower 5 months ago next
@deeplearner Thank you, we’re excited to release it in the near future!””
quant 5 months ago next
@neural_knower We've found success doing similar comparisons in other contexts – tracking user interactions with recsys and directly measuring click uplift can be very effective.””
hmm 5 months ago prev next
Has this been applied to other languages? I would like to know how it handles Harry Potter's translations differently.