├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 David Yun 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # data-science-study-bookmarks-for-korean 2 | 데이터 사이언스 공부를 위한 즐겨찾기 모음 (한국인을 위한) 3 | 4 | ## 추천 시스템 5 | 6 | ### 한글(블로그,논문) 7 | 8 | * [2년간 머신러닝 기반 실시간 추천시스템을 만든 이야기](http://withsmilo.github.io/python/2019/07/24/2_years_of_developing_personalized_real_time_recommendation_service_based_on_machine_learning/) 9 | * [[당근마켓]딥러닝 기반 개인화 추천 시스템 소개](https://medium.com/daangn/%EB%94%A5%EB%9F%AC%EB%8B%9D-%EA%B0%9C%EC%9D%B8%ED%99%94-%EC%B6%94%EC%B2%9C-1eda682c2e8c) 10 | * [[당근마켓]딥러닝 기반 개인화 추천 시스템 - production](https://medium.com/daangn/%EB%94%A5%EB%9F%AC%EB%8B%9D-%EC%B6%94%EC%B2%9C-%EC%8B%9C%EC%8A%A4%ED%85%9C-in-production-fa623877e56a) 11 | * [Interpretable Recommender System 개발 사례연구, NDC 2019](https://www.slideshare.net/ssuser593481/ndc19interpretable-recommender-system-ndc2019/ssuser593481/ndc19interpretable-recommender-system-ndc2019?fbclid=IwAR3Yg7tgnfjj0_ztBXMqD5SLM0DIrS5M3Vt_e1kEsXbP33e5Y5BRXCFxD-s) 12 | * [Pinterest Pixie 리뷰](https://ita9naiwa.github.io/recommender%20systems/2019/05/10/pixie-algotirhm-at-pinterest.html?fbclid=IwAR3SB3PBIfP_1j96p8ylT7KpgBV874Wx2i9bNItFxikGQg4xrXJ1FWEF1BE) 13 | * [nthought님의 Recommendation 블로그](http://bahnsville.tistory.com/894) 14 | * [Word2Vec 그리고 추천 시스템의 Item2Vec](https://brunch.co.kr/@goodvc78/16) 15 | * [브런치 작가 추천과 Word2Vec](https://brunch.co.kr/@goodvc78/7) 16 | * [왓챠처럼 므찌게!! 영화 추천 시스템 만들기](http://goodvc.tistory.com/9) 17 | * [딥러닝 (Tensorflow) 을 이용한 추천시스템 개발](https://www.buzzvil.com/2017/02/22/buzzvil-techblog-tensorflow-deeplearning/) 18 | * [11번가 추천 엔진 교체 (RecoPick -> Colloseo)](http://readme.skplanet.com/?p=13507) 19 | * [실시간 추천엔진 머신한대에 구겨넣기](https://www.slideshare.net/deview/261-52784785) 20 | * [딥러닝을 이용한 추천엔진 [넷플릭스 컨테스트] 코드 설명](http://keunwoochoi.blogspot.kr/2016/03/blog-post_24.html) 21 | * [논문 요약 - Deep Neural Networks for YouTube Recommendations](http://keunwoochoi.blogspot.kr/2016/09/deep-neural-networks-for-youtube.html) 22 | * [맥주마시며 만들어본 딥러닝 맥주 추천엔진](http://freesearch.pe.kr/archives/4656) 23 | * [[카카오AI리포트]내 손안의 AI 비서, 추천 알고리즘](https://brunch.co.kr/@kakao-it/72) 24 | * [[카카오AI리포트]더욱 똑똑해진 AI 광고 알고리즘](https://brunch.co.kr/@kakao-it/84) 25 | * [Discounted Cumulative Gain](http://freesearch.pe.kr/archives/1574) 26 | * [평가가 중요하다](http://www.4four.us/article/2012/04/evaluation-matters) 27 | * [Evaluating Recommender Systems - Explaining F-Score, Recall and Precision using Real Data Set from Apontador](http://aimotion.blogspot.kr/2011/05/evaluating-recommender-systems.html) 28 | * [RecoLabs 스터디 자료 Part 1](https://drive.google.com/drive/u/0/folders/0B8ETIsKCwpwXcmpZM21BbVpodGM) 29 | * [이바로님의 머신러닝/추천시스템 블로그](http://leebaro.tistory.com/category/%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D%28Machine%20Learning%29/%EC%B6%94%EC%B2%9C%20%EC%8B%9C%EC%8A%A4%ED%85%9C%28Recommendation%20System%29) 30 | * Naver 추천 관련 블로그 31 | * [취향저격 잇템 찾아주는 딥러닝 기반 개인화 상품 추천 시스템 ‘에이아이템즈 [AiTEMS]’](https://blog.naver.com/naver_search/221085553045) 32 | * [쇼핑을 자주 하지 않는 이용자도, 신규 판매자도 만족스러운 쇼핑 플랫폼을 만드는 에이아이템즈[AiTEMS]의 기술](https://blog.naver.com/naver_search/221086300708) 33 | * [CF기술•RNN기술의 장점이 융합된 ‘AiRS[에어스]’가 모바일 뉴스판에 시범 적용됩니다](https://blog.naver.com/naver_search/221105431207) 34 | * [인공지능 기반 추천 시스템 AiRS를 소개합니다](https://blog.naver.com/naver_diary/220936643956) 35 | * [인공지능 추천 시스템 AiRS 개발기 : 모델링과 시스템](https://www.slideshare.net/deview/airs-80886207) : [[Video]](http://tv.naver.com/v/2297146) 36 | * [CIKM2017에서 발표한 네이버의 자동 뉴스 추천 기술](https://blog.naver.com/PostView.nhn?blogId=naver_search&logNo=221140708518) 37 | * [Recommender Systems - Coursera Machine Learning 강의 노트](https://wikidocs.net/4916) 38 | 39 | 40 | ### 한글(노트북,Github) 41 | * [파이썬과 SQL로 배우는 추천시스템 Workshop](https://github.com/recobell/fc-rec-workshop) 42 | * [데이터 사이언스 School - 추천 시스템](https://github.com/goodvc78/fc-recsys-school) 43 | * [Word2Vec을 이용하여 브런치 작가를 추천하는 API](https://github.com/goodvc78/brunch-recsys) 44 | 45 | ### 영어 46 | 47 | * [An update on Pixie, Pinterest’s recommendation system](https://medium.com/pinterest-engineering/an-update-on-pixie-pinterests-recommendation-system-6f273f737e1b) 48 | * [PAPER : Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://labs.pinterest.com/user/themes/pin_labs/assets/paper/paper-pixie.pdf) 49 | * [YOUTUBE : Recommending 4+ Billion Ideas to 250+ Million Users in Real Time](https://www.youtube.com/watch?v=qTfeWt95EmQ) 50 | * [Recommender Systems: Matrix Operations for Fast Calculation of Similarities](https://dzone.com/articles/recommender-systems-matrix-operations-for-fast-cal) 51 | * [Quick Guide to Build a Recommendation Engine in Python](https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/) 52 | * [Apache Spark Recommendation](https://www.slideshare.net/cfregly/boston-spark-meetup-may-24-2016) 53 | * [DataEngConf SF16 - Recommendations at Instacart](https://www.slideshare.net/g33ktalk/dataengconf-sf16-recommendations-at-instacart) 54 | * [How do you build a “People who bought this also bought that”-style recommendation engine](https://datasciencemadesimpler.wordpress.com/2015/12/16/understanding-collaborative-filtering-approach-to-recommendations/) 55 | * [Recommending music on Spotify with deep learning](http://benanne.github.io/2014/08/05/spotify-cnns.html) 56 | * [Music Recommendation service with the Spotify API, Spark MLlib and Databricks](https://medium.com/@polomarcus/music-recommendation-service-with-the-spotify-api-spark-mllib-and-databricks-7cde9b16d35d) 57 | * [Deep Learning Meets Recommendation Systems](http://blog.nycdatascience.com/student-works/deep-learning-meets-recommendation-systems/) 58 | * [Recommender systems with TensorFlow](https://theintelligenceofinformation.wordpress.com/2017/05/31/recommender-systems-with-tensorflow/) 59 | * [Recommending GitHub Repositories with Google BigQuery and the implicit library](https://medium.com/@jbochi/recommending-github-repositories-with-google-bigquery-and-the-implicit-library-e6cce666c77) 60 | * [Building Simple Recommender Systems for Elasticsearch](https://qbox.io/blog/building-simple-recommender-systems-for-elasticsearch-1) 61 | * [Recommender Systems 101: Basket Analysis](http://opensourceconnections.com/blog/2016/06/06/recommender-systems-101-basket-analysis/) 62 | * [Your Recommendation Systems Aren't As Cool As My Friends](http://opensourceconnections.com/blog/2016/08/21/recommendations-systems-not-as-cool-as-friends/) 63 | * [High-Quality Recommendation Systems with Elasticsearch](http://opensourceconnections.com/blog/2016/09/09/better-recsys-elasticsearch/) 64 | * [Why I think search engines are the future of recommendation systems](http://opensourceconnections.com/blog/2016/09/13/search-engines-are-the-future-of-recsys/) 65 | * [Building Recommendation Systems with Elastic Graph](http://opensourceconnections.com/blog/2016/10/05/elastic-graph-recommendor/) 66 | * [Looking at Content Recommendation Through a Search Lens](https://www.elastic.co/blog/looking-at-content-recommendation-through-a-search-lens) 67 | * [Building a real time, solr-powered recommendation engine](https://www.slideshare.net/treygrainger/building-a-real-time-solrpowered-recommendation-engine) 68 | * [[Elasticsearch] MoreLikeThis API 설명](http://jjeong.tistory.com/1183) 69 | * [Creating a Recommender System - Part I](http://blog.stratio.com/creating-a-recommender-system-part-i/) 70 | * [Creating a Recommender System - Part II](http://blog.stratio.com/creating-recommender-system-part-two/) 71 | * [Creating an end-to-end Recommender System with Apache Spark and Elasticsearch - Nick Pentreath & Jean-François Puget](https://www.slideshare.net/sparktc/spark-ml-meedup-pentreath-puget) 72 | * [Notebook demo for Spark Summit EU 2016 Meetup in Brussels](https://github.com/MLnick/sseu16-meetup) 73 | * [Building a Scalable Recommender System with Apache Spark, Apache Kafka and Elasticsearch](http://events.linuxfoundation.org/sites/events/files/slides/ApacheBigDataEU16-NPentreath.pdf) 74 | * [Creating a Recommender System with Elasticsearch & Apache Spark approach](http://events.linuxfoundation.org/sites/events/files/slides/Apache_Big_Data_2017_Presentation.pdf) 75 | * [Multiple ways of building a recommender system with Elasticsearch](https://www.slideshare.net/vozniuk/multiple-ways-of-building-a-recommender-system-with-elasticsearch-elastic-meetup-switzerland-andrii-vozniuk) 76 | * [Recommendations Using Redis](https://dzone.com/refcardz/recommendations-using-redis) 77 | * [A movie recommendation system based on the GroupLens dataset of MovieLens data](https://github.com/osama-haggag/movie-time) 78 | * [Recommender System from tensorflow.blog](https://tensorflow.blog/tag/recommender-system/) 79 | * [Spotlight - Deep recommender models using PyTorch](https://maciejkula.github.io/spotlight/) 80 | * [Wide & Deep Learning: Better Together with TensorFlow](https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html) 81 | * [Surprise - A Python scikit for recommender systems](http://surpriselib.com/) 82 | * [Reverse Engineering The YouTube Algorithm: Part I](http://www.tubefilter.com/2016/06/23/reverse-engineering-youtube-algorithm/) 83 | * [Reverse Engineering The YouTube Algorithm: Part II](http://www.tubefilter.com/2017/02/16/youtube-algorithm-reverse-engineering-part-ii/) 84 | * [Wide & Deep Learning for MovieLens](https://github.com/CnMr236/Wide_n_deep_MovieLens) 85 | * [Tensorflow-based Recommendation systems](https://github.com/songgc/TF-recomm) 86 | * [Distance Metrics for Fun and Profit](http://www.benfrederickson.com/distance-metrics/) 87 | * [Finding Similar Music using Matrix Factorization](http://www.benfrederickson.com/matrix-factorization/) 88 | * [How do I speed up matrix factorization by sampling users without losing precision?](https://www.quora.com/How-do-I-speed-up-matrix-factorization-by-sampling-users-without-losing-precision) 89 | * [Recommendation System Algorithms](https://blog.statsbot.co/recommendation-system-algorithms-ba67f39ac9a3) 90 | * [Deep Learning Approach to Content Recommendation and It's Real World Challenges - Gil Chamiel](https://www.youtube.com/watch?v=dX1hI_PMgpo) 91 | * [Soraya Hausl - Leveraging recommender systems to personalise search results](https://www.youtube.com/watch?v=RyxJw9-yZYk) 92 | * [LightFM - Python implementation of a number of popular recommendation algorithms](http://lyst.github.io/lightfm/docs/index.html) 93 | * [Introduction to Recommender Systems](http://blog.arcbees.com/2017/01/12/introduction-recommender-systems/) 94 | * [ACM RecSys YouTube Channel](https://www.youtube.com/channel/UC2nEn-yNA1BtdDNWziphPGA) 95 | * [Recommendation Systems / Engines with TensorFlow - Google Cloud Platform User Group Singapore](https://www.youtube.com/watch?v=TNiWwaMGYzo) : [[Code]](https://github.com/karthikmswamy/RecSys) 96 | * [Alexandre Hubert: How to Improve your Recommender System with Deep Learning](https://www.youtube.com/watch?v=fQomNgNj-rg) 97 | * [Keras Implementation of Recommender Systems](https://github.com/sonyisme/keras-recommendation) 98 | * [Recommending movies with deep learning](http://blog.richardweiss.org/2016/09/25/movie-embeddings.html) 99 | * [Building a Music Recommender with Deep Learning](http://mattmurray.net/building-a-music-recommender-with-deep-learning/) 100 | * [Recommender Systems & Embeddings](https://m2dsupsdlclass.github.io/lectures-labs/slides/02_recommender_systems/index.html) : [[Code]](https://github.com/m2dsupsdlclass/lectures-labs/tree/master/labs/02_neural_recsys) 101 | * [Movix.ai — movie recommendations with Deep Learning](https://medium.com/deep-systems/movix-ai-movie-recommendations-using-deep-learning-5903d6a31607) 102 | * Data Piques's Recommendation Article 103 | * [Intro to Recommender Systems: Collaborative Filtering](http://blog.ethanrosenthal.com/2015/11/02/intro-to-collaborative-filtering/) 104 | * [Explicit Matrix Factorization: ALS, SGD, and All That Jazz](http://blog.ethanrosenthal.com/2016/01/09/explicit-matrix-factorization-sgd-als/) 105 | * [Intro to Implicit Matrix Factorization: Classic ALS with Sketchfab Models](http://blog.ethanrosenthal.com/2016/10/19/implicit-mf-part-1/) 106 | * [Embedding Everything for Anything2Anything Recommendations](https://making.dia.com/embedding-everything-for-anything2anything-recommendations-fca7f58f53ff) 107 | * [Learning to Rank Sketchfab Models with LightFM](http://blog.ethanrosenthal.com/2016/11/07/implicit-mf-part-2/) 108 | * [Using Keras' Pretrained Neural Networks for Visual Similarity Recommendations](http://blog.ethanrosenthal.com/2016/12/05/recasketch-keras/) 109 | * [Matrix Factorization in PyTorch](http://blog.ethanrosenthal.com/2017/06/20/matrix-factorization-in-pytorch/) 110 | * AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306) 111 | * [Slide](https://www.slideshare.net/AmazonWebServices/aws-reinvent-2016-using-mxnet-for-recommendation-modeling-at-scale-mac306) 112 | * [Video](https://www.youtube.com/watch?v=cftJAuwKWkA) 113 | * [Code](https://github.com/apache/incubator-mxnet/tree/master/example/recommenders) 114 | * [LibRec - A Leading Java Library for Recommender Systems](https://www.librec.net/) 115 | * [Github-recommendation-system-using-word2vec](https://github.com/zzsza/Github-recommendation-system-using-word2vec) 116 | * [Kaggle_Santander-Product-Recommendation](https://github.com/zzsza/Kaggle_Santander-Product-Recommendation) 117 | * [Kaggle_Expedia-hotel-recommendations](https://github.com/zzsza/Kaggle_Expedia-hotel-recommendations) 118 | * [From Labelling Open data images to building a private recommender system](https://www.slideshare.net/PierreGutierrez2/from-labelling-open-data-images-to-building-a-private-recommender-system) 119 | * [Applying Deep Learning to Collaborative Filtering: How Hulu builds its industry leading](http://tech.hulu.com/blog/2016/08/01/cfnade.html) 120 | * [Deep Learning with Tensorflow - Recommendation System with a Restrictive Boltzmann Machine](https://www.youtube.com/watch?v=8EaVZbmAnV0) 121 | * [Alexandros Karatzoglou: Deep Learning for Recommender Systems](https://www.youtube.com/watch?v=KZ7bcfYGuxw) : [[Slide]](https://www.slideshare.net/kerveros99/deep-learning-for-recommender-systems-budapest-recsys-meetup) 122 | * [Factorization Machines for Recommendation Systems](https://getstream.io/blog/factorization-machines-recommendation-systems/) 123 | * Understanding matrix factorization for recommendation 124 | * [Part 1 - Preliminary insights on PCA](http://nicolas-hug.com/blog/matrix_facto_1) 125 | * [Part 2 - The model behind SVD](http://nicolas-hug.com/blog/matrix_facto_2) 126 | * [Part 3 - SVD for recommendation](http://nicolas-hug.com/blog/matrix_facto_3) 127 | * [Part 4 - Algorithm implementation](http://nicolas-hug.com/blog/matrix_facto_4) 128 | * [Matrix Factorization with Tensorflow](http://katbailey.github.io/post/matrix-factorization-with-tensorflow/) 129 | * [Exploring Recommender Systems](http://blog.romanofoti.com/exploring_recommenders_movielens_dataset/) 130 | * [TensorFlow implementation of an arbitrary order Factorization Machine](https://github.com/geffy/tffm) 131 | * [How to build a movie recommender with GRAKN.AI](https://blog.grakn.ai/how-to-build-a-movie-recommender-with-grakn-ai-174d838e762d) 132 | * [Evaluating Recommender Systems](https://www.blabladata.com/2014/10/26/evaluating-recommender-systems/) 133 | * [What you wanted to know about Mean Average Precision](http://fastml.com/what-you-wanted-to-know-about-mean-average-precision/) 134 | * [Evaluating recommender systems](http://fastml.com/evaluating-recommender-systems/) 135 | * [Evaluation - python-recsys](http://ocelma.net/software/python-recsys/build/html/evaluation.html) 136 | * [HT2014 Tutorial: Evaluating Recommender Systems - Ensuring Replicability of Evaluation](https://www.slideshare.net/abellogin/ht2014-tutorial-evaluating-recommender-systems-ensuring-replicability-of-evaluation) 137 | * [How Recommendation Systems Work On Amazon & Netflix - Simplilearn Webinar](https://www.youtube.com/watch?v=BKCAkHn8jqA) 138 | * [Deep Learning for Personalized Search and Recommender Systems](https://www.slideshare.net/BenjaminLe4/deep-learning-for-personalized-search-and-recommender-systems) 139 | * [Finding similar images using autoencoders](https://medium.com/towards-data-science/find-similar-images-using-autoencoders-315f374029ea) 140 | * [Intro to Machine Learning - Building a Recommendation Model using Keras](https://www.youtube.com/watch?v=KmLJgq18r28) 141 | * [Recommender systems with TensorFlow - Google I/O Extended Bangkok 2017](https://www.youtube.com/watch?v=AMF5wYsJJus) : [[Code]](https://github.com/fooljames/tf-reco-workshop) 142 | * [SVD Recommendations using Tensorflow](https://www.bonaccorso.eu/2017/08/02/svd-recommendations-using-tensorflow/) 143 | * [Deep-Learning-for-Recommendation-Systems](https://github.com/robi56/Deep-Learning-for-Recommendation-Systems) 144 | * [Genre Essentials — Building an Album Recommender System](https://medium.com/towards-data-science/genre-essentials-building-an-album-recommender-system-c89c308d16f0) 145 | * [How Deep Neural Networks for YouTube Recommendations Work](https://www.youtube.com/watch?v=LDljD8LM9yQ) 146 | * [Deep neural networks for YouTube recommendations](https://blog.acolyer.org/2016/09/19/deep-neural-networks-for-youtube-recommendations/) 147 | * [Curated list of Recommendation System](https://handong1587.github.io/deep_learning/2015/10/09/recommendation-system.html) 148 | * [A Glimpse into Deep Learning for Recommender Systems](https://medium.com/@libreai/a-glimpse-into-deep-learning-for-recommender-systems-d66ae0681775) 149 | * [Deep Learning for Recommender Systems RecSys2017 Tutorial](https://www.slideshare.net/kerveros99/deep-learning-for-recommender-systems-recsys2017-tutorial) 150 | * [Code for our ACM RecSys 2017 paper "Personalizing Session-based Recommendation with Hierarchical Recurrent Neural Networks"](https://github.com/mquad/hgru4rec) 151 | * [Recommender Systems In Industry](https://www.slideshare.net/xamat/recommender-systems-in-industry) 152 | * [Deep AutoEncoders for Collaborative Filtering](https://github.com/NVIDIA/DeepRecommender) 153 | * [Deep NLP-based Recommenders at Finn.no](https://tech.finn.no/2017/09/08/NLP-based-recommenders-at-finn/) 154 | * [Film recommendation engine - Kaggle](https://www.kaggle.com/fabiendaniel/film-recommendation-engine/notebook) 155 | * [Public Recommendation Data - goodbooks-10k](https://github.com/zygmuntz/goodbooks-10k) 156 | * [Deep Learning in Recommender Systems - RecSys Summer School 2017](https://www.slideshare.net/balazshidasi/deep-learning-in-recommender-systems-recsys-summer-school-2017) 157 | * [How Did We Build Book Recommender Systems in an Hour Part 1 — The Fundamentals](https://medium.com/towards-data-science/how-did-we-build-book-recommender-systems-in-an-hour-the-fundamentals-dfee054f978e) 158 | * [How Did We Build Book Recommender Systems in An Hour Part 2 — k Nearest Neighbors and Matrix Factorization](https://medium.com/towards-data-science/how-did-we-build-book-recommender-systems-in-an-hour-part-2-k-nearest-neighbors-and-matrix-c04b3c2ef55c) 159 | * [Binary Representations in Recommendations](https://github.com/maciejkula/binge) 160 | * [Music Recommendations with Collaborative Filtering and Cosine Distance](https://beckernick.github.io/music_recommender/) 161 | * [Matrix Factorization for Movie Recommendations in Python](https://beckernick.github.io/matrix-factorization-recommender/) 162 | * [Building Recommender System for GitHub](https://medium.com/@andrey_lisin/building-recommender-system-for-github-a8108f0cb2bd) 163 | * [RecSys 2017 Summary](https://medium.com/towards-data-science/recsys-2017-2d0879351097) 164 | * [A Cost-Effective and Scalable Collaborative Filtering based Recommender System](https://www.bbvadata.com/cost-effective-scalable-collaborative-filtering-based-recommender-system/) 165 | * [A news recommendation engine driven by collaborative reader behavior](https://blog.insightdatascience.com/news4u-recommend-stories-based-on-collaborative-reader-behavior-9b049b6724c4) 166 | * [Spotify’s Discover Weekly: How machine learning finds your new music](https://hackernoon.com/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe) 167 | * [Approximate Nearest Neighbours for Recommender Systems](http://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/) 168 | * [Deep matrix factorization using Apache MXNet](https://www.oreilly.com/ideas/deep-matrix-factorization-using-apache-mxnet) 169 | * [LastFM Artist Recommender](http://forums.fast.ai/t/lastfm-artist-recommender-video-4-5/6302) 170 | * [Building a Real-time Recommendation Engine With Neo4j - William Lyon - OSCON 2017](https://www.youtube.com/watch?v=wbI5JwIFYEM&list=PLYXrHS_RtDZ2fbH6Ml9K5DxYBATYGeCgN) 171 | * [Evaluation in IR system [검색 시스템의 평가]](https://www.slideshare.net/MinsubYim/evaluation-in-ir-system) 172 | * [Precision, Recall, AP[Average Precision], MAP[Mean Average Precision]](https://blog.naver.com/PostView.nhn?blogId=pktoto&logNo=100093423334) 173 | * [Unranked Retrieval Evaluation](https://yunjinhan.github.io/2017/05/Unranked-Retrieval-Evaluation-Precision-and-Recall) 174 | * [accuracy, precision, recall의 차이](http://shine-ing.tistory.com/157) 175 | * [A recommender system for discovering GitHub repos, built with Apache Spark](https://github.com/vinta/albedo) 176 | * [Recommendation Papers Summary](https://github.com/gopala-kr/summary/tree/master/summaries/Week-6) 177 | * [Recommender system for education](https://www.slideshare.net/NaverEngineering/recommender-system-for-education) : [[Video]](https://www.youtube.com/watch?v=v_wnkwuoHew) 178 | * [Artwork Personalization at Netflix](https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76) 179 | * [Using Word2vec for Music Recommendations](https://towardsdatascience.com/using-word2vec-for-music-recommendations-bb9649ac2484) 180 | * [When two trends fuse: PyTorch and recommender systems](https://www.oreilly.com/ideas/when-two-trends-fuse-pytorch-and-recommender-systems) 181 | * [CTR Prediction using Spark Machine Learning Pipelines](https://www.slideshare.net/ManishaSule/ctr-prediction-using-spark-machine-learning-pipelines) 182 | * [Large-Scale Ads CTR Prediction with Spark and Deep Learning: Lessons Learned with Yanbo Liang](https://www.slideshare.net/databricks/largescale-ads-ctr-prediction-with-spark-and-deep-learning-lessons-learned-with-yanbo-liang) 183 | * How Feature Engineering can help you do well in a Kaggle competition 184 | * [Part I](https://medium.com/unstructured/how-feature-engineering-can-help-you-do-well-in-a-kaggle-competition-part-i-9cc9a883514d) 185 | * [Part II](https://medium.com/unstructured/how-feature-engineering-can-help-you-do-well-in-a-kaggle-competition-part-ii-3645d92282b8) 186 | * [Part III](https://medium.com/unstructured/how-feature-engineering-can-help-you-do-well-in-a-kaggle-competition-part-iii-f67567aaf57c) 187 | * [Kaggle's click through rate prediction with Spark Pipeline API](https://github.com/yu-iskw/click-through-rate-prediction) 188 | * [A Library for Field-aware Factorization Machines](https://github.com/guestwalk/libffm) 189 | * GCP ML Engine Sample Code 190 | * [Criteo Ctr Prediction](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/criteo_tft) 191 | * [MovieLens Recommendation](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/movielens) 192 | * [Recommender Engine — Under The Hood](https://towardsdatascience.com/recommender-engine-under-the-hood-7869d5eab072) 193 | * [Recommender Systems in Python 101](https://www.kaggle.com/gspmoreira/recommender-systems-in-python-101) 194 | * [Apache Spark Scalable Machine Learning - Lecture 4: Logistic Regression and Click-through Rate Prediction](https://docs.databricks.com/spark/1.6/training/scalable-machine-learning-cs190x-2015/module-4.html#lecture-4-logistic-regression-and-click-through-rate-prediction) 195 | * [Apache Spark - Click-Through Rate Prediction Lab](http://lncohn.com/spark/ctr.html) 196 | * [Amazon Fine Food Recommendation System PMF, SVD](https://www.kaggle.com/neocryan/amazon-fine-food-recommendation-system-pmf-svd) 197 | * Kaggle - The Movies Dataset 198 | * [The Story of Film](https://www.kaggle.com/rounakbanik/movie-recommender-systems) 199 | * [Movie Recommender Systems](https://www.kaggle.com/rounakbanik/the-story-of-film) 200 | * [Using Google Cloud Machine Learning to predict clicks at scale](https://cloud.google.com/blog/big-data/2017/02/using-google-cloud-machine-learning-to-predict-clicks-at-scale) 201 | * [GETTING ‘YA MUSIC RECOMMENDATION GROOVE ON WITH GOOGLE CLOUD PLATFORM!](https://shinesolutions.com/2017/12/15/getting-ya-music-recommendation-groove-on-with-google-cloud-platform/) 202 | * [Beyond Word2Vec Usage For Only Words](https://www.smartcat.io/blog/2017/beyond-word2vec-usage-for-only-words/) 203 | * [Various Implementations of Collaborative Filtering](https://towardsdatascience.com/various-implementations-of-collaborative-filtering-100385c6dfe0) 204 | * [Recommender Systems in Python: Beginner Tutorial](https://www.datacamp.com/community/tutorials/recommender-systems-python) 205 | * [TensorRec: A Recommendation Engine Framework in TensorFlow](https://hackernoon.com/tensorrec-a-recommendation-engine-framework-in-tensorflow-d85e4f0874e8) 206 | * [PR12-060: Deep Neural Networks for YouTube Recommendations](https://www.youtube.com/watch?v=V6zixdCIOqw) 207 | * [PR12-064: Wide&Deep Learning for Recommender Systems](https://www.youtube.com/watch?v=hKoJPqWLrI4) 208 | * [Introduction to Recommender System. Part 1 [Collaborative Filtering, Singular Value Decomposition]](https://hackernoon.com/introduction-to-recommender-system-part-1-collaborative-filtering-singular-value-decomposition-44c9659c5e75) 209 | * [Introduction to Recommender System. Part 2 [Neural Network Approach]](https://towardsdatascience.com/introduction-to-recommender-system-part-2-adoption-of-neural-network-831972c4cbf7) 210 | * [Introduction To Recommendation Systems With Deep Autoencoders](https://miguelgfierro.com/blog/2018/introduction-to-recommendation-systems-with-deep-autoencoders/) 211 | * Scaling Gradient Boosted Trees for CTR Prediction 212 | * [Part I](https://engineeringblog.yelp.com/2018/01/building-a-distributed-ml-pipeline-part1.html) 213 | * [Part II](https://engineeringblog.yelp.com/2018/01/growing-cache-friendly-trees-part2.html) 214 | * [Building a movie recommender with Factorization Machines on Amazon SageMaker](https://medium.com/@julsimon/building-a-movie-recommender-with-factorization-machines-on-amazon-sagemaker-cedbfc8c93d8) 215 | * [Machine learning movie recommender](https://github.com/klauscfhq/moviebox) 216 | * [Deep Learning for Recommender Systems](https://ebaytech.berlin/deep-learning-for-recommender-systems-48c786a20e1a) 217 | * [Recommender built using keras](https://github.com/chen0040/keras-recommender) 218 | * [DEEP BEERS: Playing with Deep Recommendation Engines Using Keras, Part 1](https://medium.com/data-from-the-trenches/deep-beers-playing-with-deep-recommendation-engines-using-keras-part-1-1efc4779568f) 219 | * [DEEP BEERS: Playing with Deep Recommendation Engines Using Keras, Part 2)](https://medium.com/data-from-the-trenches/deep-beers-playing-with-deep-recommendation-engines-using-keras-part-2-effe703ff83c) 220 | * [Insights from an evening with recommender systems experts](http://www.bibblio.org/blog/insights-evening-recommender-systems-experts) 221 | * [Building Content Recommendation Systems Using Apache MXNet and Gluon - MCL402 - re:Invent 2017](https://www.slideshare.net/AmazonWebServices/building-content-recommendation-systems-using-apache-mxnet-and-gluon-mcl402-reinvent-2017) 222 | * [Production Recommendation Systems with Cloudera](http://blog.cloudera.com/blog/2018/02/production-recommendation-systems-with-cloudera/) 223 | * [PyTorch Implementation of Session-based Recommendations with Recurrent Neural Networks](https://github.com/yhs-968/pyGRU4REC) 224 | --------------------------------------------------------------------------------