1C-Bitrix Recommendation System Module Development

The "Customers who bought this also bought" block exists in almost every online store, but its implementation often disappoints. Manual linking does not scale with a catalog of over 1,000 items. "Similar by category" yields irrelevant results: a motorcycle buyer sees other motorcycles, not helmets and gloves. A smart recommendation system based on machine learning solves this problem: it analyzes the behavior of thousands of buyers and selects products that increase the average order by 20–30% (with an average order of $3,000, that's an additional $600 per order). We develop such turnkey modules for 1C-Bitrix, ensuring transparent architecture and predictable performance. Over 7 years of work and more than 50 projects, we have accumulated the expertise to implement recommendations in 2–12 weeks, depending on complexity. A typical project cost ranges from $5,000 to $25,000 depending on scope. For example, a mid-sized store can expect an additional $120,000 in annual profit from a $15,000 investment. The module typically costs $15,000 and can save up to $400,000 annually in manager costs, providing a 26x return on investment.

Algorithm selection: collaborative or content-based filtering?

Three main approaches. Collaborative filtering — "Users who bought X also bought Y" — requires sufficient order history: works with catalogs from 500 items and order volumes from 1,000/month. The matrix is built from b_sale_basket and b_sale_order. Content-based filtering — recommendations based on similarity of characteristics: category, brand, tags, price range — works from day one without history. Hybrid recommendation approach: content-based for new items, collaborative for items with statistics. According to our data, the hybrid approach delivers 30% more clicks than pure content-based and is 1.5 times more effective. Collaborative filtering is up to 2 times more accurate than content-based for popular items, but hybrid combines both.

Why is the hybrid approach more profitable?

The hybrid approach combines the advantages of both strategies: cold start for new products and accuracy based on purchases. After accumulating 500+ orders, collaborative filtering takes over and recommends products that are often bought together. As a result, conversion increases by 25–30%, and the load on managers decreases by 40% (savings up to $400,000 per year for a catalog of 5,000 products). According to our A/B tests, hybrid recommendations achieve 1.5 times higher click-through rate than content-based alone. Hybrid recommendations are 1.5 times more effective than content-based alone, and 30% better than manual linking.

Module architecture

Recommendation block. The component receives the current product ID, reads the top-N from the table, and enriches it with data from b_iblock_element and b_catalog_price. The full query takes 5–10 ms thanks to tagged caching.

Personalization for logged-in users is built based on view history and orders. An agent calculates a personal top-20 once a day and caches it in myvendor_rec_personal. On the "Just for you" page, the component reads the cache — no heavy runtime computations.

A/B testing approach

The module includes a simple A/B test: a portion of users (by hash of user_id) sees collaborative recommendations, another sees content-based. Conversion is logged in myvendor_rec_click_log. Statistics are available in the admin panel. This allows objective evaluation of which algorithm brings more purchases. For example, in one project, conversion increased by 28% after switching to hybrid mode.

Contextual use cases

In addition to product cards and the main page, the module implements recommendations: in the cart (Add to order), on category pages (related categories), and on empty search results (alternatives by characteristics).

Development Steps

1. Data audit: Analyze current product catalog and order history.
2. Algorithm selection: Choose the best approach based on data volume.
3. Module configuration: Install and configure the recommendation module.
4. Testing: Run A/B tests to validate performance.
5. Deployment: Go live with monitoring.

What's included

• Documentation and code comments
• Access to a staging environment for testing
• One training session for administrators
• 3 months of post-launch support
• Guaranteed SLA with 4-hour response time

Comparison of approaches by criteria

Development timelines (approximate)

Cost is calculated individually, depending on catalog size and required functionality. We guarantee transparent pricing and fixed deadlines.

Increase your store's average order value: contact us to evaluate your project — we will select the optimal module configuration for your catalog and audience. Get a consultation from a certified Bitrix specialist. Request a preliminary analysis of your catalog data — it's free.

1C-Bitrix Module Development and Setup

The main trap of Bitrix is init.php. You add an OnBeforeIBlockElementUpdate handler there, then another one — a year later the file is 2000 lines, and on every hit all that code executes. We move business logic into full-fledged modules with D7 ORM, custom tables, and administrative interface. The module can be disabled, transferred to another project, covered with tests — none of that is possible with init.php. Our team has 10+ years of Bitrix experience, certified specialists, and a 6-month code guarantee. Request a consultation — we'll explain how to migrate legacy code to a modular architecture.

Why is init.php the worst place for business logic?

Init.php does not support class autoloading, lacks an isolated namespace, cannot be unit tested, and cannot be disabled without editing the file itself. Every handler written there runs on every request, even if not needed. In a module, you register handlers through EventManager, and they only execute when the event occurs. Performance difference: up to 3x with 10+ handlers.

Standard Modules: Typical Problems and Solutions

Information blocks. IBlock architecture is the first thing we review on any project. A classic mistake: one catalog infoblock with 80 properties, 30 of which are multiple. The b_iblock_element_property table swells to millions of rows, and CIBlockElement::GetList with filtering on three properties does a full scan. We move reference data to Highload-blocks, eliminate multiple properties where possible, and design the structure for 5x growth.

e-Store (sale). Cart business rules are a separate story. We set discount priorities to prevent two campaigns from giving 60% instead of 30%, connect payment handlers, and write custom validation via OnSaleOrderBeforeSaved.

Search. The built-in search module with morphology works up to 10–15 thousand elements. Beyond that — Elasticsearch. We configure it via the Bitrix search module API, indexing through CSearchFullText or custom indexers.

Highload-blocks for dictionaries, logs, user data — instead of bloated IBlocks. Direct queries via Bitrix\Highloadblock\HighloadBlockTable, custom tables instead of the EAV structure of standard infoblocks. A million records — no degradation.

Mail events. Configuration is not just templates in b_event_message. The key is SPF, DKIM, DMARC on the DNS, otherwise transactional emails go to spam. We check deliverability and set up bounce handling.

How to Design Infoblocks for Performance?

We use Highload-blocks for reference data (colors, sizes, manufacturers) that are not involved in complex queries. For SKUs — a separate infoblock with linking via IBLOCK_ELEMENT_PROPERTY. Enable INDEX_PROPERTY for frequently filtered properties. Tagged caching: when an element changes, only the related cache is cleared. Highload-blocks process up to 10x faster than infoblocks with multiple properties on volumes of 100,000 records.

Custom Module Development

Each module follows the structure /local/modules/vendor.modulename/:

• install/index.php — setup class, create tables via $DB->RunSQLBatch()
• lib/ — D7 ORM classes, extending Bitrix\Main\ORM\Data\DataManager
• admin/ — administrative pages using CAdminList, CAdminForm
• include.php — autoloading, event handler registration via EventManager::getInstance()->registerEventHandler()
• REST API endpoints via \Bitrix\Rest\RestManager

The module registers in the system, appears in the "Installed Solutions" list, and has its own settings at /bitrix/admin/settings.php?mid=vendor.modulename. It can be enabled, disabled, and updated through UpdateSystem or custom migration mechanics.

Examples of implemented tasks:

• Campaign management — visual condition builder via CAdminCalendar, timers via agents (CAgent::AddAgent), analytics linked to the sale module
• Cost calculator — React widget on the frontend, REST API in the module, formulas stored in a Highload-block
• Booking system — real-time calendar, locking via $DB->StartTransaction() / $DB->Commit() on concurrent requests, integration with channel manager via webhook

Components and Composite Cache

Component customization via result_modifier.php and component_epilog.php, not by editing template.php of the standard template. This way core updates are painless.

Composite cache ("Composite Site" technology) — the server sends ready HTML, bypassing PHP routing. Dynamic areas (cart, authorization) are loaded via CBitrixComponent::setFrameMode(true) and AJAX. TTFB drops to 30–50 ms. But there are caveats: not all components are compatible, $APPLICATION->ShowPanel() breaks composite, and careful markup of <div id="bx-composite-..."> is required.

What to Check Before Installing a Marketplace Module?

Before installing a module from the marketplace, an audit is mandatory. We check: SQL queries without prepared statements (hello SQL injection), direct use of $_REQUEST without filtering, use of outdated kernel API instead of D7, conflicts with the composite cache module. A module with no updates for over a year and a few dozen installations is likely a problem on the next PHP update. A typical case: a module calls CIBlockElement::GetList with no cache reset — the site crashes with 5000 elements.

Migration to D7

When upgrading PHP or switching to a new edition — refactor outdated calls:

• CIBlockElement::GetList() → Bitrix\Iblock\Elements\ElementTable::getList()
• CSaleOrder::GetList() → Bitrix\Sale\Order::getList()
• CModule::IncludeModule() → Bitrix\Main\Loader::includeModule() Testing on staging, rollback via git on issues.

According to official 1C-Bitrix documentation, D7 ORM is the recommended tool for working with data, providing type safety and automatic query generation.

Comparison: Init.php vs Module

Module Development Scope and Cost

What is included in module development?

• Technical specification and architectural plan
• Code following PSR-4 and Bitrix code style
• Unit tests (PHPUnit) for business logic
• Integration tests for events and REST API
• Installation, configuration, and API documentation
• Repository and documentation access
• Administrator training for module usage
• 6-month warranty support

Estimated timelines and complexity:

Cost is calculated individually — contact us for a project estimate.

Module Testing

Unit tests via PHPUnit cover business logic: discount calculation, validation, document generation. Mocks for Bitrix\Main\Application::getConnection() allow tests to be DB-independent. Integration tests verify event handlers on a real database — OnAfterIBlockElementAdd, OnSaleOrderSaved, etc. REST API endpoints are tested via curl or PHPUnit HTTP client. Critical for modules working with b_sale_order, b_catalog_price — where errors cost money.

Compatibility is checked on PHP 7.4, 8.0, 8.1, 8.2 and editions: Standard, Small Business, Business. We check conflicts with popular marketplace modules — they often intercept the same events. Load testing: measurements on 10K, 100K, 1M records, profiling via Xdebug for memory leaks and N+1 queries.

Practical Examples

Campaign module for an electronics chain. The built-in sale module discounts did not cover scenarios like "2+1", a gift with purchase over a certain amount, or combined conditions. We built a visual builder: marketers create rules via drag-and-drop without development tickets. Campaign calendar, auto-deactivation via agents, analytics linked to b_sale_order — conversion, average check, usage count. Time to launch a new campaign dropped from two days to half an hour.

Calculator for builders. Parameters (area, materials, number of floors) → formula → preliminary estimate → lead to CRM via CRest::call('crm.lead.add'). Regional coefficients and seasonal markups from a Highload-block, material prices from 1C exchange. The number of target leads increased by a third: clients see a breakdown before calling a manager.

Booking for a hotel chain. Real-time availability via AJAX requests to a custom table vendor_booking_slots, seasonal tariff calculation, synchronization with Booking.com via channel manager API. Room locking on concurrent booking via SELECT ... FOR UPDATE in transactions. Timezones handled via \DateTimeZone — a guest from Vladivostok and a manager from Moscow see the same picture.

We will evaluate your project within one day. Write to us — we'll tell you what is included in turnkey development. Contact us for a consultation on your project. Order a custom module development — get a ready solution with documentation and support.