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Surprise – a simple recommender system library for Python
1 project | reddit.com/r/Python | 1 Mar 20221 project | reddit.com/r/recommendersystems | 1 Mar 20221 project | reddit.com/r/programming | 1 Mar 20221 project | news.ycombinator.com | 1 Mar 2022
Dislike button would improve Spotify's recommendations
4 projects | news.ycombinator.com | 16 Oct 2021
I spent the latter half of 2019 trying to build this as a startup. Ultimately I pivoted (now I do newsletter recommendations instead), but if I hadn't made some mistakes I think it could've gotten more traction. Mostly I should've simplified the idea to make it easier to build. If anyone's interested in working on this, here's what I would do:
(But first some background: The way I saw it, you can split music recommendation into two tasks: (1) picking a song you already know that should be played right now, and (2) picking a new song you've never heard of before. (Music recommendation is unique in this way since in most other domains there isn't much value in re-recommending items). I think #1 is more important, and if you nail that, you can do a so-so job of #2 and still have a good system.)
Make a website that imports your Last.fm history. Organize the history into sessions (say, groups of listen events with a >= 30 minute gap in between). Feed those sessions into a collaborative filtering library like Surprise, as a CSV of `, , 1` (1 being a rating--in this case we only have positive ratings). Then make some UI that lets people create and export playlists. e.g. I pick a couple seed songs from my listening history, then the app uses Surprise to suggest more songs. Present a list of 10 songs at a time. Click a song to add it, and have a "skip all" button that gets a new list of songs. Save these interactions as ratings--e.g. if I skip a song, that's a -1 rating for this playlist. For some percentage of the suggestions (20% by default? Make it configurable), use Last.fm's or Spotify's API to pick a new song not in your history, based on the songs in the current playlist. Also sometimes include songs that were added to the playlist previously--if you skip them, they get removed from the playlist. Then you can spend a couple minutes every week refreshing your playlists. Export the playlists to Spotify/Apple Music/whatever.
As you get more users, you can do "regular" collaborative filtering (i.e. with different users) to recommend new songs instead of relying on external APIs. There are probably lots of other things you could do too--e.g. scrape wikipedia to figure out what artists have done collaborations or something. In general I think the right approach is to build a model for artist similarity rather than individual song similarity. At recommendation time, you pick an artist and then suggest their top songs (and sometimes pick an artist already in the user's history, and suggest songs they haven't heard yet--that's even easier).
This is the simplest thing I can think of that would solve my "I love music but I listen to the same old songs everyday because I'm busy and don't want to futz around with curating my music library" problem. You wouldn't have to waste time building a crappy custom music app, and users won't have to use said crappy custom music app (speaking from personal experience...). You wouldn't have to deal with music rights or integrating with Spotify/Apple Music since you're not actually playing any music.
If you want to go further with it, you could get traction first and then launch your own streaming service or something. (Reminds me a bit of Readwise starting with just highlights and then launching their own reader recently). I think it'd be neat to make an indie streaming service--kind of like Bandcamp but with an algorithm to help you find the good stuff. Let users upload and listen to their own MP3s so it can still work with popular music. Of course it'd be nicer for users in the short term if you just made deals with the big record labels, however this would help you not end up in Spotify's position of pivoting to podcasts so you can get out of paying record labels. And then maybe in a few decades all the good music won't be on the big labels anyway :).
Anyway if anyone is remotely interested in building something like this, I'll be your first user. I really need it. Otherwise I'll probably build it myself at some point in the next year or two as a side project.
Show HN: The Sample – newsletters curated for you with machine learning
1 project | news.ycombinator.com | 28 Jun 2021
I'm planning to build a business on this, so probably won't open-source it--but I'm always looking for interesting things to write about! I write a weekly newsletter called Future of Discovery; I might write up some more implementation details there in a week or two. In the mean time, most of the heavy lifting is done by the Surprise python lib. It's pretty easy to play around with, just give it a csv of , , and then you can start making rating predictions. Also fastText is easy to mess around with too. Most of the code I've written just layers things on top of that, e.g. to handle exploration-vs-exploitation as discussed in another thread here.
Recently I've been factoring out the ML code into a separate recommendation service so it can different kinds of apps (I just barely made this essay recommender system start using it for example).
I'm happy to chat about recommender systems also if you like, email's in my profile.
Level up your Python today with open-source contributions
11 projects | dev.to | 25 May 2022
Read the contributing guidelines
Should you shuffle the input for a word2vec negative sampling model before or after assigning negative context pairs for each target word?
1 project | reddit.com/r/MLQuestions | 17 May 2022
I may have a few trust issues with the shuffle argument of keras' model.fit(), after experiencing this bug regarding shuffle='batch' first hand.
2 projects | reddit.com/r/u_Animus_Vacui | 9 May 2022
Has anyone ever experienced this ? details in the comments.
1 project | reddit.com/r/learnmachinelearning | 7 May 2022
it seems like other people have had this issue like another user mentioned when using dropout or BN (https://github.com/keras-team/keras/issues/6977) , the model does use dropout so that maybe it .
How to define max_queue_size, workers and use_multiprocessing in keras fit_generator()?
1 project | reddit.com/r/codehunter | 5 May 2022
Detailed explanation of model.fit_generator() parameters: queue size, workers and use_multiprocessing
Negative dimension size caused by subtracting 3 from 1 for 'Conv2D'
2 projects | reddit.com/r/codehunter | 27 Apr 2022
I'm using Keras with Tensorflow as backend , here is my code:
1 project | reddit.com/r/learnprogramming | 25 Apr 2022
How to get reproducible results in keras
2 projects | reddit.com/r/codehunter | 13 Apr 2022
Install Keras (http://keras.io/)
20+ Free Tools & Resources for Machine Learning
5 projects | dev.to | 31 Mar 2022
Keras Keras is an API for neural networks that helps doing quick research.
Installing Python3 in Linux
9 projects | dev.to | 28 Mar 2022
According to IBM, Artificial Intelligence (AI) is technology that instructs computers to mimic the human mind in decision-making and problem-solving. Machine Learning (ML) is a subset of AI that consist of procedures that leverage on mathematical data models and algorithms to make predictions. Python implements ML and AI with generally fewer lines of code and pre-built libraries and being a scientific language also comes in support of these technologies. Some of the libraries used in AI and ML include: Tensorflow, Scikit-Learn, Numpy, Keras, Theano
What are some alternatives?
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
scikit-learn - scikit-learn: machine learning in Python
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
tensorflow - An Open Source Machine Learning Framework for Everyone
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
pytorch-lightning - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
python-recsys - A python library for implementing a recommender system