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Relevancy is subjective. Same items in search results for a “jeans” query may have a completely opposite value for you and me, as we’re different in sizes, shapes, and tastes. But leveraging past visitor behavior for LTR tasks often becomes a not so easy data engineering challenge when you want to use complex ML features in your ranking. Implementing advanced things like sliding window counters, per-item conversion CTR rates, and customer profile tracking, working both online and offline - you need a whole team of DS/DE/MLE people and a lot of time to glue things together! We got tired of reinventing the wheel of LTR again and again, and present you Metarank, an open-source personalization service handling the most typical data feature engineering tasks. It takes an event stream describing your visitor behavior, maps it to most common ML features, and reorders items in real-time to maximize the goal like CTR. All you need is a YAML config and a bit of JSON I/O. Roman Grebennikov is an independent search engineer, working on relevancy, personalization and recommendations. A pragmatic fan of open-source software, functional programming, learn-to-rank models and performance tuning.
