Audience
Retail, ecommerce
About DeepRecs
DeepRecs makes recommendations for 'Similar Products' and ‘Complete the Look’ using product images and without manual merchandising. It leverages convolutional neural networks to detect and extract feature vectors and graph visual similarities between products. Further, DeepRecs helps shoppers discover new, seasonal, niche, and long-tail products—that otherwise remain buried due to lack of historical data—using NLP algorithms that leverage catalog descriptions and other textual data.
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Particular Audience
Superhuman merchandising, product list personalization that supports the needs of your retail business. Data is the lifeblood of AI, we are the only vendor to benefit from Internet scale retail visibility. You are not the only option. Help customers find what they're looking for, to stop them going elsewhere. Our technology has the deepest understanding of products online, we process over 100 million items interpreting compatibility and similarity from behavioural comparison, attribute affinity and visual appearance. Personalize and customize every product list onsite with scalable, data-driven technology. Market dominating search and discovery applies computer vision vectors, natural language processing and see-similar item filtering technology. Enable your suppliers with paid access to high intent shoppers on the premium contextual real estate, your own site. Join the party with a single line of code and no more.
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Integrations
Company Information
Algonomy
Founded: 2020
United States
algonomy.com/omnichannel-personalization/deep-recommendations/
Videos and Screen Captures
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Product Details
Platforms Supported
Cloud
Training
Documentation
Live Online
Webinars
In Person
Videos
Support
Phone Support
24/7 Live Support
Online
DeepRecs Frequently Asked Questions
DeepRecs Product Features
eCommerce Personalization
Offers & Discounts Notifications
Personalized Site Navigation
Product Recommendations
Reporting / Analytics
Social Insights
A/B Testing
Abandoned Cart Email
Dynamic Pricing
