AI Product Recommendation Engine

الميزات الرئيسية

Behavioral signal tracking — product views, dwell time, add-to-cart and purchase events captured via frontend observers and a dedicated REST tracking endpoint, stored in custom tables

Personalized recommendation blocks for homepage, PDP and cart, delivered as themeable layout XML / block widgets placed via admin layout or templates

Frequently-bought-together logic built from real order line-item co-occurrence, recomputed on a Magento cron schedule

Seasonal and trending models that surface rising products by time window, category and store view

A/B testing of placements and strategies with conversion and AOV attribution reporting in the admin

Service contracts (PHP interfaces under api/) and a DI-based scoring engine so strategies are swappable and unit-testable

GraphQL resolvers and REST endpoints exposing recommendations for PWA Studio and headless storefronts

Full-page-cache safe rendering via a private-content / customer-data path so personalization never poisons the FPC

Multi-store-view, multi-website and customer-group aware — respects scope, segment and group pricing

Admin configuration UI under a dedicated ACL resource: placement rules, manual pins, exclusions and per-strategy weighting

Adobe Commerce extras — optional integration with customer segments and B2B company accounts where the Commerce edition is licensed

Cron-driven model rebuilds with logging plus a bin/magento CLI command to force a recompute on demand

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لا إعداد يدوي — تطبيق جاهز يبنيه ويثبّته ويدعمه ECOSIRE.

تطلب

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نبني ونثبّت

يقوم ECOSIRE ببنائه وتهيئته وتثبيته على Magento 2 الخاص بك.

الإطلاق + الدعم

تنطلق خلال 2–4 أسابيع تقريبًا، مع فترة دعم بعد الإطلاق.

عن هذا المنتج

The AI Product Recommendation Engine is a Magento 2 / Adobe Commerce extension that ECOSIRE builds to order for your catalog, then installs and supports on your store. It is not an instant Adobe Commerce Marketplace download — we engineer it around your taxonomy, themes and traffic, deliver it as a versioned module under app/code/Ecosire/ProductRecommendations, and hand over a tested, production-ready build.

Behaviorally, the module captures real signals — product views, dwell time, add-to-cart and purchase events — through frontend observers and a lightweight tracking endpoint, persisting them to dedicated tables so recommendations reflect what shoppers actually do rather than static "related products" lists. A scoring service (exposed via a service contract / DI interface) blends collaborative signals, frequently-bought-together affinity, and seasonal/trending models computed on a cron schedule.

Storefront output is delivered as themeable UI components (layout XML blocks + KnockoutJS for cart/checkout) that drop into homepage, PDP and cart, plus GraphQL and REST resolvers so PWA Studio / headless storefronts consume the same recommendations. An admin section under a dedicated ACL lets merchandisers configure placements, pin or exclude products, and run A/B tests of recommendation strategies with conversion and AOV reporting.

Everything respects multi-store-view scope, customer-group pricing and Magento's full-page cache (recommendations render via a private-content/ESI-friendly path so caching stays intact). On Adobe Commerce we can integrate with B2B and customer segments; on Open Source we deliver the equivalent natively. ECOSIRE handles installation, setup:upgrade, theme integration, and post-launch support so the engine ships clean and keeps performing.

ما تحصل عليه

Custom Magento 2 module (Ecosire_ProductRecommendations) under app/code, versioned and composer-installable, with registration.php, module.xml, di.xml and declarative schema (db_schema.xml)

Installation and configuration on your environment — setup:upgrade, di:compile, theme block placement and full smoke test on staging then production

Admin configuration guide plus a short merchandiser walkthrough of placements, pins/exclusions and A/B test setup

GraphQL/REST API documentation for headless or PWA Studio consumption of the recommendation endpoints

Source code handover with PHPCS/Magento coding-standard compliance and inline documentation

Post-launch support window with bug fixes and Magento minor-version compatibility checks, plus a maintenance option thereafter

لمن هذا

Ecommerce Manager (mid-to-large catalog)

Owns AOV and conversion targets for a 5,000+ SKU store and wants personalization that goes beyond Magento's static related/up-sell lists without ripping out the existing theme.

Magento Technical Lead / Solution Architect

Needs a clean, service-contract-based module that respects FPC, store-view scope and customer groups, exposes GraphQL for the PWA front end, and won't become unmaintainable bespoke spaghetti.

Merchandiser / CRO Specialist

Wants admin control to pin hero products, exclude clearance items, and A/B test recommendation strategies with real conversion and AOV reporting rather than guessing.

كيف يقارن AI Product Recommendation Engine.

المعيار	ECOSIRE	بناء مخصص	منافس
Personalized recommendations beyond static related/up-sell lists	متضمنة	دعم جزئي	متضمنة
Behavioral signal tracking (views, dwell, cart, purchase)	متضمنة	دعم جزئي	دعم جزئي
A/B testing of placements with AOV/conversion reporting	متضمنة	غير متضمنة	دعم جزئي
Built specifically around your catalog, theme and store-view scope	متضمنة	متضمنة	غير متضمنة
Full-page-cache safe, FPC-compatible rendering	متضمنة	دعم جزئي	دعم جزئي
GraphQL / REST for headless / PWA Studio	متضمنة	دعم جزئي	دعم جزئي
Installed, tested and supported by the builder	متضمنة	دعم جزئي	غير متضمنة
Source code handover with coding-standard compliance	متضمنة	متضمنة	دعم جزئي

الأسئلة المتداولة حول AI Product Recommendation Engine

Is this an instant download from the Adobe Commerce Marketplace?

No. This is a build-to-order extension. After purchase ECOSIRE engineers the module around your catalog, themes and traffic, then installs and configures it on your store. You receive a versioned, tested module under app/code — not a generic Marketplace package.

How long until it's live on my store?

Typical delivery is about 2 to 4 weeks from kickoff, depending on catalog size, theme complexity, headless vs. Luma, and how many placements and A/B variants you need. We scope an exact lead time after a short discovery call and build/test on staging before any production deploy.

What ongoing support and updates do I get?

The build includes a post-launch support window covering bug fixes and Magento minor-version compatibility checks. After that you can take an optional maintenance plan for upgrade compatibility (new Magento / Adobe Commerce releases), model tuning and enhancements. We don't push silent auto-updates — changes are reviewed and deployed with you.

Will it slow down my store or break full-page cache?

No. Recommendations render through a private-content / customer-data path so the full-page cache stays intact, and heavy model computation runs on cron rather than per-request. We profile the integration on staging and keep storefront rendering lightweight.

Does it work with a headless / PWA Studio storefront?

Yes. The module exposes GraphQL resolvers and REST endpoints so PWA Studio or any composable front end consumes the same recommendation logic as the Luma storefront. We document the schema and endpoints as part of the handover.