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SaaS recommender systems

 

  • 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.

 

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