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1. DMM uses Apache Spark for its recommendation system, analyzing user behavior data to provide personalized recommendations. 2. Spark enables real-time analysis through APIs while leveraging machine learning libraries like MLlib and tools like GraphX. 3. DMM discusses how it leverages various Spark features including Spark SQL, MLlib, GraphX, and integration with other technologies like Hive, Sqo
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Alternating Least Squares Method for Collaborative Filtering | Bugra Akyildiz
Recommender Systems¶An Informal Definition¶Recommender systems is a family of methods that enable filtering through large observation and information space in order to provide recommendations in the information space that user does not have any observation, where the information space is all of the available items that user could choose or select and observation space is what user experienced or o
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Recommendation and Ratings Public Data Sets For Machine Learning
gistfile1.md Movies Recommendation: MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73 Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/ Cornell University - Movie-review data for use in sentiment-
Netflix Recommendations: Beyond the 5 stars (Part 1)
by Xavier Amatriain and Justin Basilico (Personalization Science and Engineering) In this two-part blog post, we will open the doors of one of the most valued Netflix assets: our recommendation system. In Part 1, we will relate the Netflix Prize to the broader recommendation challenge, outline the external components of our personalized service, and highlight how our task has evolved with the busi
Recommendation System --Theory and Practice
This document provides an overview of recommendation systems and collaborative filtering techniques. It discusses using matrix factorization to predict user ratings by representing users and items as vectors in a latent factor space. Optimization techniques like stochastic gradient descent can be used to learn the factorization from existing ratings. The document also notes challenges of sparsity
Interactive Recommender Systems with Netflix and Spotify
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are
Detecting Near-Duplicates for Web Crawling
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Algorithms of Recommender Systems ⟨ http://www.kamishima.net/ ⟩ Release: 2016-09-26 21:53:16 +0900; 9645c3b i 2007 11 [ 07] 2008 1 [ 08a] 2008 3 [ 08b] 3 (1) (3) GitHub https://github.com/tkamishima/recsysdoc TYPO GitHub pull request issues I II III IV V ii J. Riedl J. Herlocker GroupLens WWW iii ð‘¥ ð‘‹ ð± ð—  𑥠𑦠𑋠𑌠ð± ð² 𑛠𑚠 {1, … , ð‘›}  {1, … , ð‘š} 𑦠𑦠𑥠x ð‘Ž ð‘Ÿð‘¥ð‘¦ 𑥠𑦠̄ ð‘Ÿð‘¥
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GitHub - NVIDIA/DeepRecommender: Deep learning for recommender systems
Film recommendation engine
Context-Aware Recommender Systems
Contextual User Modeling for Recommendation
Neural Word Embedding as Implicit Matrix Factorization
Item-Based Collaborative Filtering Recommendation Algorithms
Recommender Systems An Introduction Chapter02 Collaborative recommendation
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What would be a good recommendation problem from Kaggle to pick up as a short project?
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