elastic-graph-recommender - Building recommenders with Elastic Graph!

  •        23

Building recommenders with Elastic Graph! This app makes movie recommendations using Elastic graph based on the Movielens data set. Movielens is a well known open data set with user movie ratings.We use this data alongside The Movie Database(TMDB). TMDB has all the movie details such as title, image URL, etc.




Related Projects

spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

  •    Jupyter

This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens dataset to build a movie recommender using collaborative filtering with Spark's Alternating Least Saqures implementation. It is organised in two parts. The first one is about getting and parsing movies and ratings data into Spark RDDs. The second is about building and using the recommender and persisting it for later use in our on-line recommender system. This tutorial can be used independently to build a movie recommender model based on the MovieLens dataset. Most of the code in the first part, about how to use ALS with the public MovieLens dataset, comes from my solution to one of the exercises proposed in the CS100.1x Introduction to Big Data with Apache Spark by Anthony D. Joseph on edX, that is also publicly available since 2014 at Spark Summit. Starting from there, I've added with minor modifications to use a larger dataset, then code about how to store and reload the model for later use, and finally a web service using Flask.

librec - LibRec: A Leading Java Library for Recommender Systems, see

  •    Java

LibRec (http://www.librec.net) is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking. A movie recommender system is designed and available here.

spotlight - Deep recommender models using PyTorch.

  •    Python

Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models. See the full documentation for details.

winerama-recommender-tutorial - A wine recommender system tutorial using Python technologies such as Django, Pandas, or Scikit-learn, and others such as Bootstrap

  •    Python

This repository contains the code for a wine reviews and recommendations web application, in different stages as git tags. The idea is that you can follow the tutorials through the tags listed below, and learn the different concepts explained in them. The tutorials include instructions on how to deploy the web using a Koding account. However, Koding recently moved from solo to team accounts and the link provided to my Koding account deployment of the tutorial result is not working anymore. The tutorial can still be followed with no problem at all. The following tutorials will guide you through each of the previous Git tags while learning different concepts of data product development with Python.

gorse - A High Performance Recommender System Package based on Collaborative Filtering for Go

  •    Go

More examples could be found in the example folder. All models are tested by 5-fold cross validation on a PC with Intel(R) Core(TM) i5-4590 CPU (3.30GHz) and 16.0GB RAM. All scores are the best scores achieved by gorse yet.

universal-recommender - Highly configurable recommender based on PredictionIO and Mahout's Correlated Cross-Occurrence algorithm

  •    Scala

The Universal Recommender (UR) is a new type of collaborative filtering recommender based on an algorithm that can use data from a wide variety of user preference indicators—it is called the Correlated Cross-Occurrence algorithm. Unlike matrix factorization embodied in things like MLlib's ALS, CCO is able to ingest any number of user actions, events, profile data, and contextual information. It then serves results in a fast and scalable way. It also supports item properties for building flexible business rules for filtering and boosting recommendations and can therefor be considered a hybrid collaborative filtering and content-based recommender. Most recommenders can only use conversion events, like buy or rate. Using all we know about a user and their context allows us to much better predict their preferences.

crab - Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib)

  •    Python

Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recom- mendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). The engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. The project was started in 2010 by Marcel Caraciolo as a M.S.C related project, and since then many people interested joined to help in the project. It is currently maintained by a team of volunteers, members of the Muriçoca Labs.

list_of_recommender_systems - A List of Recommender Systems and Resources


Recommender systems (or recommendation engines) are useful and interesting pieces of software. I wanted to compare recommender systems to each other but could not find a decent list, so here is the one I created. Please help me keep this post up-to-date by submitting corrections and additions via pull-request, or tweet me @grahamjenson. SaaS Recommender systems have many challenges to their development including having to handle multi-tenancy, store and process a massive amount of data and other softer concerns like keeping a clients sensitive data safe on remote servers.

Recommender System for Optus Website


<Recommender System for Optus Website>This project is trying to apply some recommeder system techniques to telecom company websites. This project is an assignment for the Research Project Subject of Master of I.T. in UTS. It's developed in <C#>.

MyMediaLite - lightweight, multi-purpose library of recommender system algorithms

  •    CSharp

lightweight, multi-purpose library of recommender system algorithms

python-recsys - A python library for implementing a recommender system

  •    Python

A python library for implementing a recommender system. python-recsys is build on top of Divisi2, with csc-pysparse (Divisi2 also requires NumPy, and uses Networkx).

triplet_recommendations_keras - An example of doing MovieLens recommendations using triplet loss in Keras

  •    Jupyter

Note: a much richer set of neural network recommender models is available as Spotlight. Along the lines of BPR [1].

Recommenders - Recommender Systems

  •    Jupyter

Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on Spark, or on Azure Databricks.

lightfm - A Python implementation of LightFM, a hybrid recommendation algorithm.

  •    Python

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features).

Big Data Recommender Systems


This project seeks to explore recommender systems, including considerations of open source tools that provide recommender frameworks, a discussion of several implementation options related to recommenders, and options for making recommenders scale to large data sets using Hado...

mrec - A recommender systems development and evaluation package by Mendeley

  •    Python

mrec is a Python package developed at Mendeley to support recommender systems development and evaluation. The package currently focuses on item similarity and other methods that work well on implicit feedback, and on experimental evaluation. mrec tries to fill two small gaps in the current landscape, firstly by supplying simple tools for consistent and reproducible evaluation, and secondly by offering examples of how to use IPython.parallel to run the same code either on the cores of a single machine or on a cluster. The combination of IPython and scientific Python libraries is very powerful, but there are still rather few examples around that show how to get it to work in practice.

Recommender - CRM Personalization Engine

  •    Java

Recommender is a CRM Personalization Engine. The engine is used to make content or product recommendations that are personalized to your customers or web site visitors.

Flummi - Elastic Search HTTP REST Client

  •    Java

Flummi is a client library for Elastic Search. It has been successfully tested with Elastic Search versions 2.3, 2.4 and 5.1. It provides a comprehensive Java query DSL API and communicates with the Elastic Search Cluster via HTTP/JSON. Flummi uses HTTP and JSON for communication with Elastic Search. Its only dependencies are Gson and AsyncHttpClient, so it is good for you if you don't want to have your application depend on the full ElasticSearch JAR.

elastic - Elasticsearch client for Go.

  •    Go

Elastic is an Elasticsearch client for the Go programming language.See the wiki for additional information about Elastic.