iRecommend Reviews Scraper

Pull consumer product reviews from iRecommend.ru — Russia's largest review site (иРекомендую) — with full review bodies, the рекомендует / не рекомендует recommend verdict, star ratings, structured pros and cons, author profiles, photos, and a per-product summary that rolls up the average rating and total review count. Built for brand researchers, e-commerce sellers, and sentiment analysts who need Russian-market consumer opinion data without copy-pasting reviews page by page.

Why This Scraper?

• Full review body text, not teaser excerpts — every review is opened to its own page so you get the complete write-up (often 5,000+ characters of Russian prose), while cheaper tools stop at the short list-page preview.
• рекомендует / не рекомендует verdict on every review — iRecommend's defining signal, captured as a clean recommends true/false flag, so you can measure recommendation rate directly instead of inferring it from stars.
• Structured pros and cons — the reviewer's own достоинства and недостатки are pulled out as separate positiveNotes and negativeNotes arrays, ready to feed straight into theme tagging.
• Product summary row for every URL — productName, category breadcrumb, averageRating, total reviewCount, and a computed recommendPercent (from the like/dislike split), so you get the headline verdict without averaging reviews yourself.
• Products and reviews as separate rows — one summary row per product and one row per review, each carrying a productId back-reference, so a flagship's thousands of reviews never get crammed into a single cell.
• Author profile on every review — reviewer name, profile URL, and avatar, so you can trace prolific or influential reviewers across products and spot repeat voices.
• Photo counts and full-resolution galleries — photosCount plus every attached image URL, deduplicated across the site's thumbnail size variants so you keep one clean link per photo.
• Native Cyrillic content, UTF-8 clean — titles, bodies, categories, and author names preserved exactly as written, with no mojibake or encoding damage.
• Per-product review cap — maxReviewsPerProduct lets you pull a quick 20-review sentiment sample or the full history of a flagship product carrying 10,000+ reviews, up to a 5,000-per-product ceiling.

Use Cases

Brand & Market Research

• Measure how Russian consumers really feel about your product or a competitor's
• Track recommendation rate over time using the рекомендует / не рекомендует verdict
• Surface the most-praised and most-criticized features from structured pros and cons
• Benchmark average rating and review volume across a category

Competitive Intelligence

• Compare recommend percentages side by side across rival products
• Watch a competitor's flagship product for a sudden wave of negative reviews
• Map which product categories draw the most engaged reviewers

Product Sentiment Analysis

• Feed full review bodies into sentiment or topic models — teasers are too short to score reliably
• Build labelled datasets using the built-in recommend flag as ground truth
• Separate praise from complaints instantly via positiveNotes / negativeNotes

Marketing & Content

• Pull authentic Russian-language testimonials for localized campaigns
• Identify power reviewers and photo-rich reviews for influencer outreach
• Source real customer quotes to answer objections in product copy

E-commerce & Product Teams

• Prioritize the product fixes customers complain about most
• Monitor reviews after a launch or price change
• Enrich a product catalogue with live rating and review-count figures

Getting Started

Scrape One Product

The simplest run — paste a product page and collect its first 100 reviews plus the product summary:

{
    "startUrls": ["https://irecommend.ru/content/sait-prilozhenie-dostavki-produktov-samokat"],
    "maxReviewsPerProduct": 100
}

Quick Sentiment Sample

Grab a small, representative slice of a high-volume product for a fast read on overall sentiment:

{
    "startUrls": ["https://irecommend.ru/content/sait-prilozhenie-dostavki-produktov-samokat"],
    "maxReviewsPerProduct": 20
}

Batch Several Products, All Reviews

Compare multiple products in one run and collect every available review (up to the 5,000-per-product safety ceiling):

{
    "startUrls": [
        "https://irecommend.ru/content/sait-prilozhenie-dostavki-produktov-samokat",
        "https://irecommend.ru/content/krem-dlya-litsa-nivea",
        "https://irecommend.ru/content/smartfon-samsung-galaxy"
    ],
    "maxReviewsPerProduct": 0
}

Input Reference

What to Scrape

Parameter	Type	Default	Description
startUrls	string[]	Example URL (prefilled)	One or more iRecommend.ru product review page URLs (e.g. https://irecommend.ru/content/<name>). Each URL is scraped for its product summary and its reviews. Paste as many as you like.

Options

Parameter	Type	Default	Description
maxReviewsPerProduct	integer	100	Maximum reviews to collect per product. Set to 0 for all available reviews, up to a 5,000-per-product safety limit. A lower number keeps runs fast and affordable on popular products.

Output

Every row carries a recordType field — product or review — so you can filter the two cleanly downstream. The run also mirrors each type into its own Products and Reviews view for one-click access.

Product (recordType: "product")

One summary row per URL.

{
    "recordType": "product",
    "productId": "5814124",
    "productName": "Приложение доставки продуктов Самокат",
    "productUrl": "https://irecommend.ru/content/sait-prilozhenie-dostavki-produktov-samokat",
    "category": "Техника → Компьютеры → Компьютерные программы → Мобильное приложение",
    "averageRating": 2.7,
    "reviewCount": 851,
    "recommendPercent": 44,
    "likeCount": 375,
    "dislikeCount": 476,
    "image": "https://irecommend.ru/sites/default/files/product-images/669262/U2rWvsFwCoIcYt6JANdbDQ.png",
    "reviewsCollected": 100,
    "scrapedAt": "2026-07-24T14:30:00Z"
}

Field	Type	Description
recordType	string	Always "product"
productId	string	iRecommend internal product ID
productName	string	Product name (Cyrillic)
productUrl	string	Canonical product review page URL
category	string	Category breadcrumb, joined with → (Cyrillic)
brand	string	Brand or manufacturer, when the page lists one
averageRating	number	Average star rating, 0–5
reviewCount	integer	Total rated reviews the site shows for this product
recommendPercent	integer	Percentage of reviewers who recommend it
likeCount	integer	Reviewers who recommend
dislikeCount	integer	Reviewers who do not recommend
image	string	Product image URL
reviewsCollected	integer	How many review rows this run produced for the product
scrapedAt	string	ISO 8601 extraction timestamp

Review (recordType: "review")

One row per review, linked back to its product.

{
    "recordType": "review",
    "reviewId": "10582612",
    "productId": "5814124",
    "productName": "Приложение доставки продуктов Самокат",
    "productUrl": "https://irecommend.ru/content/sait-prilozhenie-dostavki-produktov-samokat",
    "reviewUrl": "https://irecommend.ru/content/minus-500-tysyach-i-plyus-10-kg-za-god-somnitelnye-podarki-ot-kurerov-i-kak-za-neskolko-let",
    "title": "Минус 500 тысяч и плюс 10 кг за год. Сомнительные подарки от курьеров.",
    "text": "Добрый день! Знакомство с сервисом случилось в 2021 году, когда Самокат только-только появился в моем родном Ярославле и мы с супругом как раз переехали...",
    "teaser": "Добрый день! Знакомство с сервисом случилось в…",
    "rating": 5,
    "recommends": true,
    "author": {
        "name": "Zguglagolom",
        "url": "https://irecommend.ru/users/zguglagolom",
        "avatar": "https://cdn-irec.r-99.com/sites/default/files/imagecache/60i/pictures/31/picture-3175184-OjZolky0.jpg"
    },
    "created": "2025-03-19T15:15:11+01:00",
    "commentsCount": 36,
    "photosCount": 17,
    "images": [
        "https://cdn-irec.r-99.com/sites/default/files/imagecache/copyright/user-images/3175184/P5Cu79fjARXIeEaZsYg.jpg"
    ],
    "positiveNotes": ["Акции и скидки", "Бесплатная доставка", "Удобство приложения"],
    "negativeNotes": ["Иногда приезжает просрочка", "Выбор разнообразный, но еще маленький"],
    "scrapedAt": "2026-07-24T14:30:00Z"
}

Field	Type	Description
recordType	string	Always "review"
reviewId	string	iRecommend review ID
productId	string	Parent product ID (back-reference)
productName	string	Parent product name (Cyrillic)
productUrl	string	Parent product review page URL
reviewUrl	string	Direct URL to this review
title	string	Review headline (Cyrillic)
text	string	Full review body text (Cyrillic)
teaser	string	Short list-page excerpt
rating	integer	Star rating, 1–5
recommends	boolean	true for рекомендует, false for не рекомендует
author	object	{ name, url, avatar } for the reviewer (Cyrillic name)
created	string	Publication date (ISO 8601)
commentsCount	integer	Comments left on the review
photosCount	integer	Photos attached to the review
images	string[]	Full-resolution photo URLs, size-variant deduplicated
positiveNotes	string[]	The reviewer's listed pros (достоинства)
negativeNotes	string[]	The reviewer's listed cons (недостатки)
scrapedAt	string	ISO 8601 extraction timestamp

Tips for Best Results

• Sample before you commit. On a high-volume product, run maxReviewsPerProduct: 20 first to gauge overall sentiment, then raise the cap only if you need the full set — flagship products can carry well over 10,000 reviews.
• Set the cap to 0 only when you mean it. 0 collects everything up to the 5,000-per-product ceiling; on a popular product that is a large run, so reserve it for products where you truly need full coverage.
• Batch related products in one run. Paste several competing products together to build a like-for-like comparison set in a single pass.
• Use recommends as your sentiment ground truth. It is the reviewer's own explicit verdict — cleaner and more decisive than trying to threshold the star rating.
• Read positiveNotes and negativeNotes first. These reviewer-authored pros and cons are the fastest route to recurring themes before you parse the full text.
• Split your dataset by recordType. Keep product rows for headline metrics and review rows for detail; join them on productId when you need both.
• Point at the exact product page. Use the /content/<name> review page for a product, not a category or brand landing page, so the summary and reviews line up.

Pricing

From $0.50 per 1,000 reviews + $4.00 per 1,000 products — you pay for each product summary collected and each review collected, nothing else. Reviews are the high-volume item and priced low so large sentiment pulls stay affordable.

Charged per	Gold price	Per row
Product summary	$4.00 per 1,000 products	$0.004
Review	$0.50 per 1,000 reviews	$0.0005

Estimate a run by adding the two together. Typical mixes at the Gold rate:

Example run	Products	Reviews	Estimated cost
10 products × 50 reviews	10	500	$0.29
1 flagship × 5,000 reviews	1	5,000	$2.50
100 products × 100 reviews	100	10,000	$5.40
1,000 products × 100 reviews	1,000	100,000	$54.00

Bronze, Silver, and Gold loyalty subscribers pay progressively less, applied automatically in the Console — the table above shows the lowest (Gold) tier. No compute or time-based charges — you pay per result (product + review), plus a small fixed per-run start fee. See the Pricing tab for the current rate at your discount tier.

Integrations

Export data in JSON, CSV, Excel, XML, or RSS. Connect to 1,500+ apps via:

• Zapier / Make / n8n — Workflow automation
• Google Sheets — Direct spreadsheet export
• Slack / Email — Notifications on new results
• Webhooks — Trigger custom APIs on run completion
• Apify API — Full programmatic access

Legal & Ethical Use

This actor is designed for legitimate market research, brand monitoring, sentiment analysis, and competitive intelligence. Users are responsible for complying with applicable laws and iRecommend.ru's terms of service, and for handling any personal data (such as reviewer names) in line with relevant privacy regulations. Do not use extracted data for spam, harassment, or any illegal purpose.