grahamjenson / list_of_recommender_systems

List of Recommender Systems 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. Software as a Service Recommender Systems 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. The benefits to using a SaaS recommender system is that you can pay for value with a low overhead rather than having a large upfront investment, they generally have a clear integration path for you to use, and they provide continual development and improvement while you use it. The SaaS recommender systems are: • Peerius closed, product and e-commerce focused for live and email recommendations. Active and seems very interesting, although little information about the actual product and how it works is available. • Strands is a closed, product and e-commerce focused system. I think it works by including tracking scripts (a la Google Analytics) on the website, and recommendations widgets. What I really like about Strands is their publishing of case-studies e.g. Wireless Emporium and white papers like The Big promise of recommender systems. Although these do not discuss the exact solutions provided, they give a good overview of their vision and goals of providing recommendations. • SLI Systems Recommender A closed recommender system focused on e-commerce, search and mobile. • Using Hadoop on Google Cloud an example use of Google cloud with benchmarks from recommender system. • ParallelDots tool to relate published content • Amazon Machine Learning machine learning platform to model data and create predictions • Azure ML machine learning platform to model data and create predictions • Gravity R&D is a company built by some of the winners from the 2009 Netflix prize. They offer a solution that provides targeted, customized recommendations to users of websites. They have some pretty big clients including DailyMotion and a technology page which describes their architecture, algorithms, and a list of publications. (suggested by Marton Vetes) • Dressipi Style Adviser is a clothing-specific recommendation service. It incorporates both expert domain knowledge and machine learning to find outfits for occasions or moods. • Sajari is a search, recommendation and matching (e.g. dating website) service. On their site, they also have aggregated a bunch of useful data-sets. • IBM Watson is available through Watson Developer Cloud, which provides REST APIs (Watson APIs on Bluemix) and SDKs that use cognitive computing to solve complex problems. • Recombee provides REST API, SDKs for multiple languages and graphical user interface for evaluating results. Main features are real time model updates, easy to use query language for filtering and boosting according to complex business rules and advanced features such as options for getting diverse or rotated recommendations. Recombee offers instant account with 100k free recommendation requests per month. • Segmentify Recommendation Engine, Personalization and Real-Time Analytics tool. • Mr. DLib A recommender-system as-a-service for academic organisations such as digital libraries and reference managers. Mr. DLib provides 'related-article' recommendations, is open-source, and publishes most of it's data. • Rumo is a flexible SaaS recommendation system adaptable to all entertainment industries (films, music, podcasts, video games, sports, etc.) and based on both content metadata and user behaviors. Rumo's algorithms are transparent and explainable, providing full control over the recommendation process. • Froomle is a modular recommendation platform, which focuses on serving news and e-commerce companies. They offer a variety of modules, optimized on their use case (f.e. discovery or related), business goal (f.e. CTR or conversion) and integration type (web, mail or push notifications). Their modules use state of the art machine learning techniques under the hood. • Recommendations AI deliver highly personalized product recommendations at scale. It's a part of Google Cloud’s Discovery Solutions for Retail which provide personalized search and recommendations. Open Source Recommender Systems Most of the non-SaaS recommender systems that are open-source. This may have been because recommender systems are more tailored to clients so not easily made into a product. The open-source recommender systems are: • The Universal Recommender Is built on the modern Correlated Cross-Occurrence Algorithm that uses many indicators of user taste, and so can target most use cases. Source on github built-in to the Harness ML server or as a template for the older PredictionIO server (highest-rated template). Active and commercially supported. • PredictionIO is built on technologies Apache Spark, Apache HBase and Spray. It is a machine learning server that can be used to create a recommender system. The source can be located on github. Main repository has been *abandoned*. • Raccoon Recommendation Engine is an open source Node.js based collaborative filter that uses Redis as a store. It is effectively abandoned. • HapiGER is an open source Node.js collaborative filtering engine, which can use in-memory, PostgreSQL or rethinkdb. Reasonably active development (when I have time :) • EasyRec Java and Rest based recommendations. Abandoned • Mahout Hadoop/linear algebra based data mining • Seldon is a Java based prediction engine built on technologies like Apache Spark. It provides a demo movie recommendations application here. • Oryx v2 a large scale architecture for machine learning and prediction (suggested by Lorand) • RecDB is a PostgreSQL ex…