MovieRecommender VS TIMDB

Compare MovieRecommender vs TIMDB and see what are their differences.

Our great sponsors
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • WorkOS - The modern identity platform for B2B SaaS
  • SaaSHub - Software Alternatives and Reviews
MovieRecommender TIMDB
4 1
32 35
- -
0.0 0.0
over 1 year ago 11 months ago
Jupyter Notebook 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.

MovieRecommender

Posts with mentions or reviews of MovieRecommender. We have used some of these posts to build our list of alternatives and similar projects.

TIMDB

Posts with mentions or reviews of TIMDB. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing MovieRecommender and TIMDB you can also consider the following projects:

tutorials - AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

next-movie - Pick your next movie using Next.js 13

apartment_recommender_streamlit_app - Streamlit App that recommends apartments in Seattle using the Airbnb kaggle dataset: https://www.kaggle.com/code/rdaldian/airbnb-content-based-recommendation-system/data?select=listings.csv

cinemagoer - Cinemagoer is a Python package useful to retrieve and manage the data of the IMDb (to which we are not affiliated in any way) movie database about movies, people, characters and companies

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

Federated-Recommendation-Neural-Collaborative-Filtering - Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.

Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

PopCritic - Movies Reviewed by people, for people

recommenders - Best Practices on Recommendation Systems

perplex - A Movie Renamer for Plex Metadata

Artificial-Intelligence-Projects - Collection of Artificial Intelligence projects.

watch3r - Movie watchlist and journal app built with Vue 3 and Fauna DB. All future development of this project has moved to Codeberg.