Search engines rely on models, which rank the matching results for a given user query. These models optimize the order of items. They learn how to rank items in a result list, therefore the name Learning-to-Rank (LTR) models.
You have kafka as your message broker up and running and you may wonder: In which format should I send my data around? Maybe the string format pops up in your mind. Why not just put all fields into a long string and separate them with a comma?
In this blogpost you will get a basic understanding about message brokers. We will look at two very popular message brokers, Kafka and RabbitMQ, and learn, how they handle messages.
This post will teach you the inution of REST APIs and how you can use them to get interesting datasets for your data projects. First, we will look at the four components of a request. In the second part of this blogpost, we will go through one example and access the coingecko API via
In many scenarios, such as a google search or a product recommendation in an online shop, we have tons of data and limited space to display it. We cannot show all the products of an online shop to the user as a possible next best offer. Neither would a user want to scroll through all the pages indexed by a search engine to find the most relevant page that matches his search keywords. The most relevant content should be on top. Learning to rank (LTR) models are supervised machine learning models that attempt to optimize the order of items. So compared to classification or regression models, they do not care about exact scores or predictions, but the relative order. LTR models are typically applied in search engines, but gained popularity in other fields such as product recommendations as well.
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