movie-recommender
Movie recommender system based on Non-Negative Matrix Factorization and Singular Value Decomposition, with a Flask web interface (by lorenanda)
implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets (by benfred)
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movie-recommender | implicit | |
---|---|---|
1 | 3 | |
6 | 3,420 | |
- | - | |
6.1 | 6.2 | |
over 3 years ago | about 1 month ago | |
Python | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
movie-recommender
Posts with mentions or reviews of movie-recommender.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-06-06.
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4 tips for creating an impressive data science portfolio on GitHub
For example, for my research project on psych-verbs I wrote a paper-like README targeted at academics/fellow linguists, whereas for my movie recommender system I wrote an informal short description and included a screencast, aimed at a general audience.
implicit
Posts with mentions or reviews of implicit.
We have used some of these posts to build our list of alternatives
and similar projects.
- Recommendation system integration
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Content-based Recommender System with Python
Although CF methods also have some explainability available. CF library https://github.com/benfred/implicit which I used a lot in my past projects, e.g. has the method model.explain available for that.
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Tensorflow Recommender (TFRS) or Scikit-Surprise?
In that case, you are doing some form of collaborative filtering, though you can also add content-based filtering as additional features later. You can use either implicit or explicit feedback. I would suggest checking this package, and this tutorial. Let me know if you have any other questions.
What are some alternatives?
When comparing movie-recommender and implicit you can also consider the following projects:
recommendation-algorithm - Collaborative filtering recommendation system. Recommendation algorithm using collaborative filtering. Topics: Ranking algorithm, euclidean distance algorithm, slope one algorithm, filtragem colaborativa.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.