iRecommend Product Reviews Scraper

Scrapes product reviews from iRecommend.ru. Provide a product reviews page URL, and the actor extracts detailed review data, handling pagination automatically.

Key Features

• URL-Based Scraping: Starts from a full iRecommend product reviews page URL.
• Automatic Pagination: Crawls through all review pages until finished or until a specified limit is reached.
• Comprehensive Data Extraction: Captures structured data for each review, including:
  • Review title, teaser text, and star rating (1-5).
  • Author details (name, profile URL, avatar).
  • Review images and photo gallery links.
  • Publication date, comment count, and unique IDs.
  • Timestamp of when the data was scraped.

How to Use

1. Find the URL: Navigate to iRecommend.ru, find a product's review section, and copy the full URL from your browser.
   • Example URL: https://irecommend.ru/content/sait-prilozhenie-dostavki-produktov-samokat
2. Configure the Actor: Provide the URL in the searchUrl input field. Optionally, set a maxItems limit.
3. Run: Execute the actor. It will scrape reviews and output them as a structured dataset.

Input

Output

The actor outputs a dataset where each item is a JSON object representing a single review.

Example Output Item:

{
  "searchUrl": "https://irecommend.ru/content/sait-prilozhenie-dostavki-produktov-samokat",
  "reviewId": 10582612,
  "productId": 5814124,
  "vid": 18213605,
  "reviewUrl": "https://irecommend.ru/content/minus-500-tysyach...",
  "title": "Review title here...",
  "teaser": "Review preview text...",
  "images": ["https://cdn-irec.r-99.com/.../image1.jpg", "..."],
  "morePhotosText": "+14 фото",
  "morePhotosUrl": "https://irecommend.ru/content/...",
  "author": {
    "name": "Zguglagolom",
    "url": "https://irecommend.ru/users/zguglagolom",
    "avatar": "https://cdn-irec.r-99.com/.../avatar.jpg"
  },
  "created": "19.03.2025",
  "rating": 5,
  "commentsCount": 29,
  "photosCount": 17,
  "scrapedAt": "2025-11-10T06:42:02.777Z"
}

Use Cases

Common applications include product sentiment analysis, competitive research, customer feedback aggregation, and review content mining.