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    "# Reccomender System\n",
    "\n",
    "- [Machine Learning for Recommender systems — Part 1 (algorithms, evaluation and cold start)](https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed)\n",
    "- https://www.datacamp.com/community/tutorials/recommender-systems-python"
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    "Broadly, recommender systems can be classified into 3 types:\n",
    "\n",
    "- **Simple recommenders**: offer generalized recommendations to every user, based on movie popularity and/or genre. The basic idea behind this system is that movies that are more popular and critically acclaimed will have a higher probability of being liked by the average audience. IMDB Top 250 is an example of this system.\n",
    "- **Content-based recommenders**: suggest similar items based on a particular item. This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations. The general idea behind these recommender systems is that if a person liked a particular item, he or she will also like an item that is similar to it.\n",
    "- **Collaborative filtering engines**: these systems try to predict the rating or preference that a user would give an item-based on past ratings and preferences of other users. Collaborative filters do not require item metadata like its content-based counterparts."
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    "## IMDB example\n",
    "\n",
    "1. Decide on the metric or score to rate movies on.\n",
    "2. Calculate the score for every movie.\n",
    "3. Sort the movies based on the score and output the top results."
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