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Artificial Intelligence for the user experience.

A world of personalized recommendations, to discover products you love.

HS.Recommend is a powerful REST API recommendation platform, that is based on machine learning to generate personalized suggestions for each user.

Using data on users preferences, their past interactions with products, and other relevant factors, the HS.Recommend engine leverages machine learning models to identify patterns, correlations, and trends.

Based on these analyses, HS.Recommend generates personalized recommendations for each user. These recommendations take also into account factors such as market trends, behaviors of similar users, and other contextual information.

HS.Recommend helps optimize marketing and sales strategies, enabling businesses to target relevant products and maximize cross-selling and upselling opportunities. Additionally, it enhances product discovery, allowing users to easily find relevant items that may have otherwise gone unnoticed.

Finally, HS.Recommend can be easily integrated into existing applications and services through its web APIs, making its implementation simple and flexible.

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Through machine learning, HS.Recommend analyzes user behavior patterns and identifies correlations between their preferences and the features of your products. This allows the platform to provide accurate and relevant recommendations, enhancing the user experience and facilitating the discovery of new products.

Constantly evolving and updating, the HS.Recommend engine exploits various logical approaches to suggest products. Learn more by reading the following paragraphs!

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Chosen for you

Using sophisticated algorithms, HS.Recommend identifies patterns and relationships between users and products, identifying tastes, trends and preferences. This interaction data is then combined with information about purchases made by users with similar profiles, enabling the recommendation system to make highly relevant recommendations.

For example, if a user has shown interest in a certain genre of movies or purchased similar products in the past, HS.Recommend might suggest related movies or products based on the behaviors of users with similar interests. This approach based on interaction and purchase data from similar users enables the creation of a personalized experience for each user, enhancing new product discovery and maximizing overall user satisfaction.

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Visual similarities

The recommendation system using visual similarity between products helps users discover new products based on appearance. This feature is based on the analysis of visual characteristics of products, such as shape, color, style, and design.

Through machine vision and machine learning systems, the system can recognize extract the distinctive visual features of a product and compare them with those of other products in the catalog.

Based on visual similarity, the recommendation system suggests similar products that might interest the user. This visual similarity approach overcomes the limitations of text descriptions and enables users to find similar products more intuitively and immediately. It also helps discover products that might not have been considered otherwise, providing a more visually engaging search and discovery experience.

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Smart navigation

For anonymous users, or no user profiling was available, HS.Recommend allows browsing of the product catalog with innovative search modes, allowing the user personalized control over product recommendation based on their specific preferences.

In fact, a user can select search attributes and determine the relative importance of each attribute in product recommendation. For example, if a user wants to find a movie that is a balanced combination of action and comedy, he or she can enter the attributes “comedy” and “action” and adjust them in intensity. The recommendation system will take into account the preferences indicated by the user and try to suggest films that fit these criteria.

This feature allows users to customize the recommendation experience to their specific preferences and get recommendations that more closely reflect their individual tastes, providing a more accurate and satisfying product discovery experience.

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