Argoid.ai secures patent-pending status for noise-reduction in product & content recommendations, enhancing customer experience & site ‘individualization’

Argoid.ai, creators of the game-changing AI-powered recommendation system for e-commerce and OTT, recently filed a patent for their groundbreaking invention. This enhances Argoid’s software, enabling them to offer an unrivaled recommendation and personalization experience for users.

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The publication of the patent application covers the developments in Argoid’s technology that significantly reduces noise in product and content recommendations. By adopting new techniques of product and content representation and utilizing Natural Language Processing, irrelevant items are successfully sifted out. This involves major improvements in interactions between computers and human language.

For online shoppers, this means that only the most compatible products or content are recommended; based, not only on what they are searching for, but also on their buying behavior and other context-based information such as the current season.

Argoid’s software is particularly adept with e-commerce and OTT sites, as the software recognises vast amounts of product and content attributes.

Noise can take various forms; for example, recommendations displaying blazers in a collection of shirts, or content recommendations of movie genres not preferred by the user. Even summer fashion products appearing during winter in a particular location, may be considered noise.

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Noise-reducing technology creates a seamless customer journey from the first click, as users find the ideal product more efficiently, without distractions, leading to a superior ‘individualized’ customer experience, and higher revenues.

The application for exclusive rights demonstrates Argoid’s commitment to developing innovative technologies for e-commerce stores and OTT platforms, as the system creates recommendations of remarkable accuracy and quality. It makes possible a customer experience tailored to each individual’s preferences, without ‘noise’, and at scale.