How do price comparison websites organize product data?

Frontend
1

I’m curious about the backend process of price comparison websites. How do they manage to group identical products from different stores onto a single page? There are two main possibilities I can think of:

  1. Do they maintain their own product database with details and images, then crawl other sites to match and group items using SQL queries based on things like name or SKU?

  2. Or do they skip having a preset product list, instead crawling everything and grouping results directly through SQL queries?

I’d really appreciate any insights into how this works behind the scenes. It seems like a tricky thing to get right, especially with all the different ways products can be named or described across various stores. Thanks for any help explaining this!

2

hey, i think they got a mix: a product db and constant crawling to update data. they use clash algorithms to match names, specs, pics etc. but it’s not perfect, so duplicates can slip in.

3

hmmm, interesting question! how do u think they handle product variations? like different colors or sizes? do they group those together or list em separately? i wonder if they use AI to help with matching products across sites. what’s ur guess on how accurate their groupings usually are?

4

Price comparison websites typically employ a hybrid approach to organize product data effectively. They maintain a core product database with standardized information, which serves as a foundation. This database is continually updated through web crawling and data feeds from partnered retailers. Sophisticated matching algorithms are utilized to correlate incoming product data with existing entries, considering factors like brand names, model numbers, and specifications. Natural language processing techniques help normalize product descriptions across different sources. Additionally, many sites implement machine learning models to improve matching accuracy over time. Despite these advanced methods, manual curation is often necessary to handle edge cases and ensure data quality. This multi-faceted approach allows for comprehensive product coverage while striving for accuracy in comparisons.