Recommender System

That Delivers Accurate Real-Time Predictions At Scale

Vizle

It’s a recommendation engine we developed for leveraging logical analysis with the help of technology in improving user personalization, recommendations, actionable insights, marketing automation and more. Vizle helps to apply unsupervised machine learning techniques to datasets accurately and generates recommendations based on the available information, seamlessly. For instance, Sigmaways recommender allows a scalable search & suggestions based on multiple attributes in media like Genre, Popularity, Timeline, ratings etc. so that users can access the platform without any explicit input.

Personalized Experience

Easy To Integrate Recommender System

User API

Vizle offers user API that supports
  • User Register
  • User Clear
  • User History

Recommendation API

We offer recommendation specific API that supports
  • User-Specific Recommendation
  • Language Filtering
  • Trending Recommendations To First-Time Users

Event API

Event-specific API that supports
  • Product Add
  • Purchaser
  • Ratings
  • View

Unsupervised Learning

Content-based filtering using kNN algorithm
  • User Register
  • User Clear
  • User History

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    Why Vizle?

    An Autonomous System Delivering User-Centric Experience

    Media Recommendations

    Based On Metadata attributes such as genre, cast, program, and schedule delivering relevant user experience across touchpoints

    External Sources

    Data enrichment through third party sources like IMDb supported by AI

    Contextual Search

    Real-time search personalization backed by a ranking algorithm

    User Information & Real-Time Insights

    Suggestions based on user preferences and demographic information, providing real-time user activity insights for smart user engagement

    Churn Prevention

    Subscriber prediction and churn reduction with integrated machine learning models

    Media Recommendations

    Based On Metadata attributes such as genre, cast, program, and schedule delivering relevant user experience across touchpoints

    External Sources

    Data enrichment through third party sources like IMDb supported by AI

    Contextual Search

    Real-time search personalization backed by a ranking algorithm

    User Information & Real-Time Insights

    Suggestions based on user preferences and demographic information, providing real-time user activity insights for smart user engagement

    Churn Prevention

    Subscriber prediction and churn reduction with integrated machine learning models

    Unmatched Outcomes

    Accelerated Succes Rates

    Decreased Bounce Rate

    Increased
    Time-Spend

    Added Value
    For Subscriber

    Increase In
    Revenue Rates

    Engineer Your Digital Tomorrow, Today