HotBits Python API
Surprise
| HotBits Python API | Surprise | |
|---|---|---|
| - | 9 | |
| 2 | 6,427 | |
| - | - | |
| 0.0 | 5.1 | |
| almost 5 years ago | 6 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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HotBits Python API
We haven't tracked posts mentioning HotBits Python API yet.
Tracking mentions began in Dec 2020.
Surprise
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Ask HN: Interesting recent papers in recommender systems?
I'm trying my hand at implementing a recommender system for mainly tabular data (think song ratings based on tempo, genre, etc.) using the algorithms in Surprise [1] as a baseline.
I assume there are plenty of "throw an LLM at it" papers out there, but are there any interesting architectures/results people have found in "plain" recommender systems?
In particular I'd be interested to see how XAI techniques are applied in user-facing recommenders (e.g. "tempo matters a lot to your song ratings")
[1] https://surpriselib.com/
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Recommender Systems: Surprise library installation on m1 mac
Something is wrong with the repo. The compiler fails with this error clang: error: no such file or directory: 'surprise/similarities.c' If you go to the repo, you'll see the file is indeed missing: https://github.com/NicolasHug/Surprise/tree/master/surprise
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Recommender systems question
Scikit-surprise is a useful package and has pretty good documentation to help make the leap from conceptual understanding to code. If you want to understand the various implementations, the package is open source and available on GitHub. I can’t speak for optimal computational efficiency but I think that it’s premature to worry about that while you’re still making the transition from concept to functionality.
- Surprise – a simple recommender system library for Python
- Dislike button would improve Spotify's recommendations
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Show HN: The Sample – newsletters curated for you with machine learning
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[1]; 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[2]. It's pretty easy to play around with, just give it a csv of , , and then you can start making rating predictions. Also fastText[3] 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[4] start using it for example).
I'm happy to chat about recommender systems also if you like, email's in my profile.
[1] https://findka.com
[2] http://surpriselib.com/
[3] https://fasttext.cc/
[4] https://essays.findka.com
What are some alternatives?
PyBrain
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
MLflow - Open source platform for the machine learning lifecycle
tensorflow - An Open Source Machine Learning Framework for Everyone
xeger - Library to generate random strings from regular expressions.
python-recsys - A python library for implementing a recommender system
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
Crab - Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).