> For the complete documentation index, see [llms.txt](https://docs.abtasty.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.abtasty.com/recommendations-and-merchandising_deprecated/recos-and-merch-algorythms/better-understanding.md). # Better understanding ABTasty recommendations are made up of default algorithms that are trained using data from your integrations (analytics, catalogue, CMS, etc.). \ When a recommendation banner is displayed on a website, the website code calls the ABTasty Recos servers with input parameters (such as the article displayed on the page, the articles seen by the visitor, etc.) to retrieve the relevant list of articles in the given context. \ This call applies the algorithm contained in the recommendation to the data provided. \ For example, you could create a recommendation banner (recoID X24) which displays similar items on a product page (TSHIRT1) filtered on the items the visitor has already seen (TSHIRT24, TSHIRT49). \ The page code will then call the Recos server with the following entries:\ reco = X24\ viewed\_item = TSHIRT1 (the SKU of the product currently viewed)\ viewed\_items = \[TSHIRT24, TSHIRT49] (SKUs of previously viewed products)\ The server will retrieve the configuration of reco X24 and call the algorithm it contains, ‘Similar items’ to product TSHIRT1, then filter the results of this algorithm to subtract TSHIRT24, TSHIRT49. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.abtasty.com/recommendations-and-merchandising_deprecated/recos-and-merch-algorythms/better-understanding.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.