{
  "id": 372781,
  "title": "Kaggle's Top Recommender Systems Notebooks",
  "url": "/competitions/otto-recommender-system/discussion/372781",
  "author_name": "",
  "post_date": "2022-12-18T00:39:53.780873700Z",
  "votes": 23,
  "comment_count": 1,
  "views": 0,
  "content": "<h2>Kaggle's Top Recommender Systems Notebooks</h2>\n<h4>The most upvoted Recommender system notebooks</h4>\n<p>Below is a list of some of the most upvoted recommender systems notebooks on Kaggle.</p>\n<p>Enjoy! </p>\n<hr>\n<ul>\n<li><a href=\"https://www.kaggle.com/rajmehra03/cf-based-recsys-by-low-rank-matrix-factorization\" target=\"_blank\">CF Based RecSys by Low Rank Matrix Factorization</a></li>\n<li><a href=\"https://www.kaggle.com/fuzzywizard/rec-sys-collaborative-filtering-dl-techniques\" target=\"_blank\">Rec Sys -&gt; Collaborative Filtering &amp; DL Techniques</a></li>\n<li><a href=\"https://www.kaggle.com/sharthz23/pandas-scipy-for-recsys\" target=\"_blank\">Pandas &amp; SciPy for RecSys</a></li>\n<li><a href=\"https://www.kaggle.com/vbookshelf/ikigai-a-career-village-recsys\" target=\"_blank\">Ikigai - A Career Village RecSys</a></li>\n<li><a href=\"https://www.kaggle.com/shahrukhkhan/rec-sys-neural-collaborative-filtering-pytorch\" target=\"_blank\">Rec Sys: Neural Collaborative Filtering PyTorch</a></li>\n<li><a href=\"https://www.kaggle.com/akshayt19nayak/part-ii-tag-recsys-cosine-levenshtein-dist\" target=\"_blank\">Part II: Tag RecSys - Cosine + Levenshtein Dist</a></li>\n<li><a href=\"https://www.kaggle.com/aashishbidap/recsys2015-analysis\" target=\"_blank\">Recsys2015- Analysis</a></li>\n<li><a href=\"https://www.kaggle.com/matanivanov/wide-deep-learning-for-recsys-with-pytorch\" target=\"_blank\">Wide &amp; Deep Learning for RecSys with Pytorch</a></li>\n<li><a href=\"https://www.kaggle.com/jaketuricchi/baseline-userrating-prediction-for-recsys\" target=\"_blank\">Baseline UserRating Prediction for RecSys</a></li>\n<li><a href=\"https://www.kaggle.com/shubhamrawat4402/recsys-2015\" target=\"_blank\">Recsys-2015</a></li>\n<li><a href=\"https://www.kaggle.com/danofer/2015-recsys-challenge-starter\" target=\"_blank\">2015 RecSys Challenge - Starter</a></li>\n<li><a href=\"https://www.kaggle.com/aleksandrbychkov/polina-aleksandr-recsystem\" target=\"_blank\">Polina&amp;Aleksandr recsystem</a></li>\n<li><a href=\"https://www.kaggle.com/ariwansrisetya/movies-content-based-recsys\" target=\"_blank\">movies content based recsys</a></li>\n<li><a href=\"https://www.kaggle.com/kerneler/starter-recsys-challenge-2015-1ec81474-0\" target=\"_blank\">Starter: RecSys Challenge 2015 1ec81474-0</a></li>\n<li><a href=\"https://www.kaggle.com/skyprince213/recsys-ecom\" target=\"_blank\">RecSys-ECom</a></li>\n<li><a href=\"https://www.kaggle.com/niharika41298/netflix-visualizations-recommendation-eda\" target=\"_blank\">🔴Netflix Visualizations, Recommendation, EDA🍿</a></li>\n<li><a href=\"https://www.kaggle.com/fabiendaniel/film-recommendation-engine\" target=\"_blank\">Film recommendation engine</a></li>\n<li><a href=\"https://www.kaggle.com/kanncaa1/recommendation-systems-tutorial\" target=\"_blank\">Recommendation Systems Tutorial</a></li>\n<li><a href=\"https://www.kaggle.com/yclaudel/recommendation-engine-with-networkx\" target=\"_blank\">Recommendation engine with networkx</a></li>\n<li><a href=\"https://www.kaggle.com/danielbecker/careervillage-org-recommendation-engine\" target=\"_blank\">CareerVillage.org Recommendation Engine</a></li>\n<li><a href=\"https://www.kaggle.com/tanetboss/user-clustering-for-anime-recommendation\" target=\"_blank\">User Clustering for anime recommendation</a></li>\n<li><a href=\"https://www.kaggle.com/erikbruin/movie-recommendation-systems-for-tmdb\" target=\"_blank\">Movie recommendation systems for TMDB</a></li>\n<li><a href=\"https://www.kaggle.com/niyamatalmass/lightfm-hybrid-recommendation-system\" target=\"_blank\">LightFM Hybrid Recommendation system</a></li>\n<li><a href=\"https://www.kaggle.com/erikbruin/recommendations-to-passnyc-1st-place-solution\" target=\"_blank\">Recommendations to PASSNYC (1st place solution)</a></li>\n<li><a href=\"https://www.kaggle.com/shawamar/product-recommendation-system-for-e-commerce\" target=\"_blank\">Product Recommendation System for e-commerce</a></li>\n<li><a href=\"https://www.kaggle.com/vatsalmavani/music-recommendation-system-using-spotify-dataset\" target=\"_blank\">Music Recommendation System using Spotify Dataset</a></li>\n<li><a href=\"https://www.kaggle.com/ambarish/eda-and-recommendation-system-donors-choose\" target=\"_blank\">EDA and Recommendation System Donors Choose</a></li>\n<li><a href=\"https://www.kaggle.com/eward96/netflix-recommendation-engine\" target=\"_blank\">Netflix Recommendation Engine</a></li>\n</ul>\n<blockquote>\n  <p><strong>Credit:</strong> <a href=\"https://www.kaggle.com/discussions/getting-started/295525\" target=\"_blank\">Original Post</a></p>\n</blockquote>",
  "messages": [
    {
      "id": "2068459",
      "postDate": "12/18/2022 00:39:53",
      "content": "<h2>Kaggle's Top Recommender Systems Notebooks</h2>\n<h4>The most upvoted Recommender system notebooks</h4>\n<p>Below is a list of some of the most upvoted recommender systems notebooks on Kaggle.</p>\n<p>Enjoy! </p>\n<hr>\n<ul>\n<li><a href=\"https://www.kaggle.com/rajmehra03/cf-based-recsys-by-low-rank-matrix-factorization\" target=\"_blank\">CF Based RecSys by Low Rank Matrix Factorization</a></li>\n<li><a href=\"https://www.kaggle.com/fuzzywizard/rec-sys-collaborative-filtering-dl-techniques\" target=\"_blank\">Rec Sys -&gt; Collaborative Filtering &amp; DL Techniques</a></li>\n<li><a href=\"https://www.kaggle.com/sharthz23/pandas-scipy-for-recsys\" target=\"_blank\">Pandas &amp; SciPy for RecSys</a></li>\n<li><a href=\"https://www.kaggle.com/vbookshelf/ikigai-a-career-village-recsys\" target=\"_blank\">Ikigai - A Career Village RecSys</a></li>\n<li><a href=\"https://www.kaggle.com/shahrukhkhan/rec-sys-neural-collaborative-filtering-pytorch\" target=\"_blank\">Rec Sys: Neural Collaborative Filtering PyTorch</a></li>\n<li><a href=\"https://www.kaggle.com/akshayt19nayak/part-ii-tag-recsys-cosine-levenshtein-dist\" target=\"_blank\">Part II: Tag RecSys - Cosine + Levenshtein Dist</a></li>\n<li><a href=\"https://www.kaggle.com/aashishbidap/recsys2015-analysis\" target=\"_blank\">Recsys2015- Analysis</a></li>\n<li><a href=\"https://www.kaggle.com/matanivanov/wide-deep-learning-for-recsys-with-pytorch\" target=\"_blank\">Wide &amp; Deep Learning for RecSys with Pytorch</a></li>\n<li><a href=\"https://www.kaggle.com/jaketuricchi/baseline-userrating-prediction-for-recsys\" target=\"_blank\">Baseline UserRating Prediction for RecSys</a></li>\n<li><a href=\"https://www.kaggle.com/shubhamrawat4402/recsys-2015\" target=\"_blank\">Recsys-2015</a></li>\n<li><a href=\"https://www.kaggle.com/danofer/2015-recsys-challenge-starter\" target=\"_blank\">2015 RecSys Challenge - Starter</a></li>\n<li><a href=\"https://www.kaggle.com/aleksandrbychkov/polina-aleksandr-recsystem\" target=\"_blank\">Polina&amp;Aleksandr recsystem</a></li>\n<li><a href=\"https://www.kaggle.com/ariwansrisetya/movies-content-based-recsys\" target=\"_blank\">movies content based recsys</a></li>\n<li><a href=\"https://www.kaggle.com/kerneler/starter-recsys-challenge-2015-1ec81474-0\" target=\"_blank\">Starter: RecSys Challenge 2015 1ec81474-0</a></li>\n<li><a href=\"https://www.kaggle.com/skyprince213/recsys-ecom\" target=\"_blank\">RecSys-ECom</a></li>\n<li><a href=\"https://www.kaggle.com/niharika41298/netflix-visualizations-recommendation-eda\" target=\"_blank\">🔴Netflix Visualizations, Recommendation, EDA🍿</a></li>\n<li><a href=\"https://www.kaggle.com/fabiendaniel/film-recommendation-engine\" target=\"_blank\">Film recommendation engine</a></li>\n<li><a href=\"https://www.kaggle.com/kanncaa1/recommendation-systems-tutorial\" target=\"_blank\">Recommendation Systems Tutorial</a></li>\n<li><a href=\"https://www.kaggle.com/yclaudel/recommendation-engine-with-networkx\" target=\"_blank\">Recommendation engine with networkx</a></li>\n<li><a href=\"https://www.kaggle.com/danielbecker/careervillage-org-recommendation-engine\" target=\"_blank\">CareerVillage.org Recommendation Engine</a></li>\n<li><a href=\"https://www.kaggle.com/tanetboss/user-clustering-for-anime-recommendation\" target=\"_blank\">User Clustering for anime recommendation</a></li>\n<li><a href=\"https://www.kaggle.com/erikbruin/movie-recommendation-systems-for-tmdb\" target=\"_blank\">Movie recommendation systems for TMDB</a></li>\n<li><a href=\"https://www.kaggle.com/niyamatalmass/lightfm-hybrid-recommendation-system\" target=\"_blank\">LightFM Hybrid Recommendation system</a></li>\n<li><a href=\"https://www.kaggle.com/erikbruin/recommendations-to-passnyc-1st-place-solution\" target=\"_blank\">Recommendations to PASSNYC (1st place solution)</a></li>\n<li><a href=\"https://www.kaggle.com/shawamar/product-recommendation-system-for-e-commerce\" target=\"_blank\">Product Recommendation System for e-commerce</a></li>\n<li><a href=\"https://www.kaggle.com/vatsalmavani/music-recommendation-system-using-spotify-dataset\" target=\"_blank\">Music Recommendation System using Spotify Dataset</a></li>\n<li><a href=\"https://www.kaggle.com/ambarish/eda-and-recommendation-system-donors-choose\" target=\"_blank\">EDA and Recommendation System Donors Choose</a></li>\n<li><a href=\"https://www.kaggle.com/eward96/netflix-recommendation-engine\" target=\"_blank\">Netflix Recommendation Engine</a></li>\n</ul>\n<blockquote>\n  <p><strong>Credit:</strong> <a href=\"https://www.kaggle.com/discussions/getting-started/295525\" target=\"_blank\">Original Post</a></p>\n</blockquote>",
      "rawMarkdown": "## Kaggle's Top Recommender Systems Notebooks\n#### The most upvoted Recommender system notebooks\n\nBelow is a list of some of the most upvoted recommender systems notebooks on Kaggle.\n\nEnjoy! \n\n_____\n\n- [CF Based RecSys by Low Rank Matrix Factorization](https://www.kaggle.com/rajmehra03/cf-based-recsys-by-low-rank-matrix-factorization)\n- [Rec Sys -> Collaborative Filtering & DL Techniques](https://www.kaggle.com/fuzzywizard/rec-sys-collaborative-filtering-dl-techniques)\n- [Pandas & SciPy for RecSys](https://www.kaggle.com/sharthz23/pandas-scipy-for-recsys)\n- [Ikigai - A Career Village RecSys](https://www.kaggle.com/vbookshelf/ikigai-a-career-village-recsys)\n- [Rec Sys: Neural Collaborative Filtering PyTorch](https://www.kaggle.com/shahrukhkhan/rec-sys-neural-collaborative-filtering-pytorch)\n- [Part II: Tag RecSys - Cosine + Levenshtein Dist](https://www.kaggle.com/akshayt19nayak/part-ii-tag-recsys-cosine-levenshtein-dist)\n- [Recsys2015- Analysis](https://www.kaggle.com/aashishbidap/recsys2015-analysis)\n- [Wide & Deep Learning for RecSys with Pytorch](https://www.kaggle.com/matanivanov/wide-deep-learning-for-recsys-with-pytorch)\n- [Baseline UserRating Prediction for RecSys](https://www.kaggle.com/jaketuricchi/baseline-userrating-prediction-for-recsys)\n- [Recsys-2015](https://www.kaggle.com/shubhamrawat4402/recsys-2015)\n- [2015 RecSys Challenge - Starter](https://www.kaggle.com/danofer/2015-recsys-challenge-starter)\n- [Polina&Aleksandr recsystem](https://www.kaggle.com/aleksandrbychkov/polina-aleksandr-recsystem)\n- [movies content based recsys](https://www.kaggle.com/ariwansrisetya/movies-content-based-recsys)\n- [Starter: RecSys Challenge 2015 1ec81474-0](https://www.kaggle.com/kerneler/starter-recsys-challenge-2015-1ec81474-0)\n- [RecSys-ECom](https://www.kaggle.com/skyprince213/recsys-ecom)\n- [🔴Netflix Visualizations, Recommendation, EDA🍿](https://www.kaggle.com/niharika41298/netflix-visualizations-recommendation-eda)\n- [Film recommendation engine](https://www.kaggle.com/fabiendaniel/film-recommendation-engine)\n- [Recommendation Systems Tutorial](https://www.kaggle.com/kanncaa1/recommendation-systems-tutorial)\n- [Recommendation engine with networkx](https://www.kaggle.com/yclaudel/recommendation-engine-with-networkx)\n- [CareerVillage.org Recommendation Engine](https://www.kaggle.com/danielbecker/careervillage-org-recommendation-engine)\n- [User Clustering for anime recommendation](https://www.kaggle.com/tanetboss/user-clustering-for-anime-recommendation)\n- [Movie recommendation systems for TMDB](https://www.kaggle.com/erikbruin/movie-recommendation-systems-for-tmdb)\n- [LightFM Hybrid Recommendation system](https://www.kaggle.com/niyamatalmass/lightfm-hybrid-recommendation-system)\n- [Recommendations to PASSNYC (1st place solution)](https://www.kaggle.com/erikbruin/recommendations-to-passnyc-1st-place-solution)\n- [Product Recommendation System for e-commerce](https://www.kaggle.com/shawamar/product-recommendation-system-for-e-commerce)\n- [Music Recommendation System using Spotify Dataset](https://www.kaggle.com/vatsalmavani/music-recommendation-system-using-spotify-dataset)\n- [EDA and Recommendation System Donors Choose](https://www.kaggle.com/ambarish/eda-and-recommendation-system-donors-choose)\n- [Netflix Recommendation Engine](https://www.kaggle.com/eward96/netflix-recommendation-engine)\n\n\n> **Credit:** [Original Post](https://www.kaggle.com/discussions/getting-started/295525)",
      "votes": null
    },
    {
      "id": "2075145",
      "postDate": "12/25/2022 05:58:56",
      "content": "<p>great summary!! thx a lot</p>",
      "rawMarkdown": "great summary!! thx a lot",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2075145,
      "author_name": "yanggechen",
      "author_url": "",
      "post_date": "12/25/2022 05:58:56",
      "content": "<p>great summary!! thx a lot</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2068459": "## Kaggle's Top Recommender Systems Notebooks\n#### The most upvoted Recommender system notebooks\n\nBelow is a list of some of the most upvoted recommender systems notebooks on Kaggle.\n\nEnjoy! \n\n_____\n\n- [CF Based RecSys by Low Rank Matrix Factorization](https://www.kaggle.com/rajmehra03/cf-based-recsys-by-low-rank-matrix-factorization)\n- [Rec Sys -> Collaborative Filtering & DL Techniques](https://www.kaggle.com/fuzzywizard/rec-sys-collaborative-filtering-dl-techniques)\n- [Pandas & SciPy for RecSys](https://www.kaggle.com/sharthz23/pandas-scipy-for-recsys)\n- [Ikigai - A Career Village RecSys](https://www.kaggle.com/vbookshelf/ikigai-a-career-village-recsys)\n- [Rec Sys: Neural Collaborative Filtering PyTorch](https://www.kaggle.com/shahrukhkhan/rec-sys-neural-collaborative-filtering-pytorch)\n- [Part II: Tag RecSys - Cosine + Levenshtein Dist](https://www.kaggle.com/akshayt19nayak/part-ii-tag-recsys-cosine-levenshtein-dist)\n- [Recsys2015- Analysis](https://www.kaggle.com/aashishbidap/recsys2015-analysis)\n- [Wide & Deep Learning for RecSys with Pytorch](https://www.kaggle.com/matanivanov/wide-deep-learning-for-recsys-with-pytorch)\n- [Baseline UserRating Prediction for RecSys](https://www.kaggle.com/jaketuricchi/baseline-userrating-prediction-for-recsys)\n- [Recsys-2015](https://www.kaggle.com/shubhamrawat4402/recsys-2015)\n- [2015 RecSys Challenge - Starter](https://www.kaggle.com/danofer/2015-recsys-challenge-starter)\n- [Polina&Aleksandr recsystem](https://www.kaggle.com/aleksandrbychkov/polina-aleksandr-recsystem)\n- [movies content based recsys](https://www.kaggle.com/ariwansrisetya/movies-content-based-recsys)\n- [Starter: RecSys Challenge 2015 1ec81474-0](https://www.kaggle.com/kerneler/starter-recsys-challenge-2015-1ec81474-0)\n- [RecSys-ECom](https://www.kaggle.com/skyprince213/recsys-ecom)\n- [🔴Netflix Visualizations, Recommendation, EDA🍿](https://www.kaggle.com/niharika41298/netflix-visualizations-recommendation-eda)\n- [Film recommendation engine](https://www.kaggle.com/fabiendaniel/film-recommendation-engine)\n- [Recommendation Systems Tutorial](https://www.kaggle.com/kanncaa1/recommendation-systems-tutorial)\n- [Recommendation engine with networkx](https://www.kaggle.com/yclaudel/recommendation-engine-with-networkx)\n- [CareerVillage.org Recommendation Engine](https://www.kaggle.com/danielbecker/careervillage-org-recommendation-engine)\n- [User Clustering for anime recommendation](https://www.kaggle.com/tanetboss/user-clustering-for-anime-recommendation)\n- [Movie recommendation systems for TMDB](https://www.kaggle.com/erikbruin/movie-recommendation-systems-for-tmdb)\n- [LightFM Hybrid Recommendation system](https://www.kaggle.com/niyamatalmass/lightfm-hybrid-recommendation-system)\n- [Recommendations to PASSNYC (1st place solution)](https://www.kaggle.com/erikbruin/recommendations-to-passnyc-1st-place-solution)\n- [Product Recommendation System for e-commerce](https://www.kaggle.com/shawamar/product-recommendation-system-for-e-commerce)\n- [Music Recommendation System using Spotify Dataset](https://www.kaggle.com/vatsalmavani/music-recommendation-system-using-spotify-dataset)\n- [EDA and Recommendation System Donors Choose](https://www.kaggle.com/ambarish/eda-and-recommendation-system-donors-choose)\n- [Netflix Recommendation Engine](https://www.kaggle.com/eward96/netflix-recommendation-engine)\n\n\n> **Credit:** [Original Post](https://www.kaggle.com/discussions/getting-started/295525)",
    "2075145": "great summary!! thx a lot"
  },
  "source": "meta"
}