Recommendation engines companies, products & suppliers
Item-to-user recommenders (products, content, media) as the product. Jobs: item recommend; similar-item; feed ranking.
What are Recommendation engines?
Item-to-user recommenders (products, content, media) as the product. Jobs: item recommend; similar-item; feed ranking.
What problems does it solve?
Catalogs still show the same bestsellers to everyone.
Typical business use cases
- Item recommend
- Similar-item
- Feed ranking
Important capabilities
- Models
- Cold start
- Feedback loops
What buyers should evaluate
- Holdout
- Filter bubbles
- Sensitive attributes
Risks and governance considerations
Recommendations that leak another user's behaviour.
Procurement checklist
- Holdout
- Attributes
- Isolation
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Products and providers
Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.
By Algolia
Algolia AI Recommendations is a machine-learning recommendation engine for ecommerce storefronts and content sites. It uses behavioural signals such as clicks and conversions to po
Amazon Personalize is an AWS machine learning service for building recommendation systems. Developers use their own interaction, item and user data to train models for real-time pe
By Bloomreach
Bloomreach Discovery product recommendations help ecommerce teams show personalised product suggestions using search history, browse and customer data, product data and the company
By Clerk.io
Clerk.io is Clerk.io's software product, from Clerk.io ApS. Published at clerk.io. Directory filing: Recommendation engines. Vendor legal name on this product record: Clerk.io ApS.
By Constructor
The only site search and product discovery built for ecommerce KPIs. Delivering superior experiences with AI, NLP, data and personalization. Constructor Recommendations is Construc
By Coveo
Generative search & relevant recommendations. The leading platform for GenAI, AI agents, commerce, customer service, workplace & website experiences. Coveo ML Product Recommendatio
Dynamic Yield Experience OS is Dynamic Yield's software product, from Dynamic Yield Ltd. Published at dynamicyield.com/platform. Directory filing: Personalisation platforms. Vendor
By Google
AI Commerce Search is Google Cloud's product search, browse and recommendations service for retailers, formerly Vertex AI Search for commerce and now part of Gemini Enterprise for
By Klevu
Klevu has united with Searchspring and Intelligent Reach under Athos Commerce to transform ecommerce search, merchandising, and personalization. Klevu Smart Recommendations is Klev
By Monetate
Monetate is the #1 experience optimization platform designed to help enterprise brands and digital businesses scale KPIs and wow customers. Monetate is Monetate's software product,
By Nosto
Boost sales and loyalty with Nosto’s AI-powered ecommerce personalization, merchandising, and search tools. Nosto Product Recommendations is Nosto's software product, from Nosto So
By NVIDIA
NVIDIA Merlin is an open-source framework for building high-performing recommender systems at scale. It gives data scientists, machine learning engineers and researchers libraries,
By Recombee
Recommender as a Service with an intuitive RESTful API & SDKs tailored by data scientists. Recombee Recommendation API is Recombee's model API, from Recombee, s.r.o. Published at d
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- AI Commerce Search· Google
- Algolia AI Recommendations· Algolia
- Amazon Personalize· Amazon Web Services
- Bloomreach Discovery Product Recommendations· Bloomreach
- Clerk.io· Clerk.io
- Constructor Recommendations· Constructor
- Coveo ML Product Recommendations· Coveo
- Klevu Smart Recommendations· Klevu
- Nosto Product Recommendations· Nosto
- NVIDIA Merlin· NVIDIA
- Recombee Recommendation API· Recombee
Also used in this category
These products have a different primary category so they do not compete for the same ranking queries. They are listed here because buyers still encounter them in this job.
- Constructor AI Commerce Search and Discovery· Constructor
- Dynamic Yield Experience OS· Dynamic Yield
- Monetate· Monetate
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Recommendation engines?
Item-to-user recommenders (products, content, media) as the product. Jobs: item recommend; similar-item; feed ranking.
What should not be listed as Recommendation engines?
Products whose buyer job is personalisation platforms, merchandising, or next-best-action decisioning. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Recommendation engines supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What are Recommendation engines?
Item-to-user recommenders (products, content, media) as the product. Jobs: item recommend; similar-item; feed ranking.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.
Does a TrustMark on one product cover the rest of the company?
No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.
What incident-handling evidence is useful for AI suppliers?
Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.