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Jstarcraft Rns ⭐ 368

专注于解决推荐领域与搜索领域的两个核心问题:排序预测(Ranking)和评分预测(Rating). 为相关领域的研发人员提供完整的通用设计与参考实现. 涵盖了70多种排序预测与评分预测算法,是最快最全的Java推荐与搜索引擎.

Recdb Postgresql ⭐ 262

RecDB is a recommendation engine built entirely inside PostgreSQL

Recommender ⭐ 249

A C library for product recommendations/suggestions using collaborative filtering (CF)

Movielens ⭐ 202

4 different recommendation engines for the MovieLens dataset.

Recommendationsystem ⭐ 190

Book recommender system using collaborative filtering based on Spark

Autoencoders_cf ⭐ 155

Collaborative modeling for recommendation. Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.

Collaborative Filtering ⭐ 114

Implemented Item, User and Hybrid based Collaborative Filtering

Deep Autoencoders For Collaborative Filtering ⭐ 111

Using Deep Autoencoders for predictions of movie ratings.

Movie Recommender System ⭐ 86

Basic Movie Recommendation Web Application using user-item collaborative filtering.

Slopeone ⭐ 84

PHP implementation of the Weighted Slope One rating-based collaborative filtering scheme.

Second Springboard Capstone Project: A Movie Recommender

Recommender System On Movielens Dataset ⭐ 69

Knowledge-based, Content-based and Collaborative Recommender systems are built on MovieLens dataset with 100,000 movie ratings. These Recommender systems were built using Pandas operations and by fitting KNN, SVD & deep learning models which use NLP techniques and NN architecture to suggest movies for the users based on similar users and for queries specific to genre, user, movie, rating, popularity.

Top K Rec ⭐ 54

A method collection for top-k recommendation

CF4J: Collaborative Filtering for Java

Movie Recommendation Netflix ⭐ 29

🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧

Moodle Mod_ratingallocate ⭐ 20

🙏 "Fair allocation" moodle activity

This is the official code for the WSDM 2021 paper: 'Local Collaborative Autoencoders.'

Datascienceprojects ⭐ 17

Sample data science projects (machine learning, optimization, business intelligence)

Collaborativefiltering Rbm ⭐ 16

A variation of the Restricted Boltzmann Machine (RBM) for Collaborative Filtering tasks

Movie Recommender Using Rbm ⭐ 16

A Movie Recommender System using Restricted Boltzmann Machine (RBM), approach used is collaborative filtering.

Trustsvd ⭐ 15

TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings

Recommender Addon ⭐ 11

A node addon for recommendations

Deeprecommender ⭐ 11

Training Deep AutoEncoders for Collaborative Filtering

User Based Collaborative Filtering ⭐ 11

User-based Collaborative Filtering in Python

Collaborativefiltering ⭐ 11

simple c++ implementation of collaborative filtering.

New algorithms for Large-scale Collaborative Ranking: PrimalCR and PrimalCR++

Bigdata_project_recommender_system ⭐ 10

Recommender system based on Item Collaborative Filtering and MapReduce

My Yelper ⭐ 10

Building a Hybrid Recommendation Engine: Content-based Filtering + Collaborative Filtering (Spark, PySpark, Apache Drill)

It556_worthless_without_coffee_da Iict_final_project ⭐ 9

This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.

Movies Recommender ⭐ 8

A system to recommend movies according to ratings provided by users using Collaborative Filtering Learning Algorithm.

C++ implementation of primalCR and primalCR++ proposed in KDD'17 paper

Movie_recommender_system_scala ⭐ 6

Build a movie recommender system using Collaborative Filtering by leveraging Spark in Scala

Movie Recommendation Engines ⭐ 6

Writing recommendation systems for movies, and performing data analysis on movie datasets to gain valuable insights.

Hive App - find what you need close to you, create offers with your current location updated in real-time for people nearby

Recommender Systems ⭐ 5

Different implementations of recommender systems.

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