Interesting article! Shopify's approach is cool, it's interesting they're using Kafka to generate datasets. I wonder if the explicit human rankings will get stale (and also be hugely outweighted by implicit judgements in the training data). The real-time feedback aspect sounds cool, I wonder if it's just for metrics or also for re-training in real-time.<p>I worked on a Learning To Rank implementation a year or so ago. What struck me then (and now reading about Shopify's implementation) is that the approach is often very similar across sites, but the implementation is usually rather tailored. You see the same patterns: online/offline metrics; nDCG; click models and implicit/explicit relevance judgements; re-ranking top-k of results, and so on.<p>Unfortunately there doesn't seem to be a technology tying all of the components of an LtR system together. A managed service like Algolia could be an answer. I wonder if industry will eventually converge on a framework, such as an extension to Open Source Connection's Elasticsearch Learning to Rank plugin (<a href="https://diff.wikimedia.org/2017/10/17/elasticsearch-learning-to-rank-plugin/" rel="nofollow">https://diff.wikimedia.org/2017/10/17/elasticsearch-learning...</a>).<p>It's a really interesting area of theory and practice - I hope Shopify write more about their implementation!<p>I'd also recommend reading Airbnb's really excellent paper - <a href="https://arxiv.org/pdf/1810.09591.pdf" rel="nofollow">https://arxiv.org/pdf/1810.09591.pdf</a>.