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Recommendation Engine Development

AI-powered Recommendation engines that accurately predict future outcomes

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    Our Clients

    Adapting to business needs in multiple domains with our Recommendation engines

    According to the statistics provided by McKinsey and Tech Emergence, implementing recommendation systems in their business operations brought Amazon 35 percent of its revenue and 23.7 percent growth to BestBuy. 75 percent of video consumption on Netflix comes from its recommendation system. Similarly, 60 percent of the views on YouTube come from their recommendation feature.

    We build custom recommendation engines to enable our clients with WHAT, WHEN, WHERE, and WHOM to recommend their products, services, and applications. Our AI driven engines use machine learning and deep learning algorithms to serve the purpose and provide relevant information. We help in determining which recommendation system is best suited for our clients.

    We make sure that our recommending systems help you with-

    • Increased business revenues
    • Personalized customer experience and retention
    • Enhanced customer service
    • Accurate analytics reports to the clients
    • Personalized marketing campaigns with productive insights

    Filtering Models of our Recommendation Engines

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    Collaborative filtering

    Collaborative filtering

    These algorithms generate recommendations based on crowd-sourced inputs, where customers’ affinity define the similarities. This filtering increases further engagements from the users as they build strong connections with every piece of content, users share.

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    Content-based filtering

    Content-based filtering

    These algorithms generate recommendations based on crowd-sourced inputs, where the customers’ affinity defines the similarities. Various models are built to process various types of attribute data. The approach requires the implementation of market research data, and hence, no user ratings are required.

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    Demographic-based recommendation systems

    Demographic-based recommendation systems

    We categorize the users based on attributes and facilitate recommendations based on a set of demographic classes. We build demographic recommendation systems that are not complex and are easy to implement. The approach requires market research data to be fully implemented and hence, no user ratings are required.

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    Hybrid recommendation systems

    Hybrid recommendation systems

    We integrate any of the above two models that are best suited for a particular industry and build hybrid models that provide more effective recommendations to the users. These systems help improve recommendations from inadequate, scant, and infrequent datasets.

    Featured Use Cases of Recommendation Engines

    Product recommendation engines
    Product recommendation engines recommend new products to customers based on their previous search or purchasing behavior. These engines filter, predict and show items that a user would like to purchase and allow for natural, logical upsell and cross-sell opportunities.
    Health and wellness recommendation systems
    Health and wellness recommendation systems specialized doctors based on the symptoms provided, recommend yoga and other wellness-related services, etc. Leveraging big data and predictive analytics, the engines help people make informed decisions about their health.

    WHY [ SOULPAGE ]

    Soulpage is a Hyderabad, India based data science technology company with deep authority over AI, Machine Learning and NLP, etc. We provide end-to-end services from strategy consulting to software development. We work with innovation-driven enterprises, idea-driven entrepreneurs and new-age startups partner to make sense from their data, get actionable insights and gain competitive advantage. We follow complete adherence to data regulations like HIPAA, Data Protection 2018, etc.

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