Optimizing Recommendation Systems with JDK’s Vector API

**Optimizing Recommendation Systems with JDK’s Vector API**

By Harshad SaneRanker is one of the largest and most complex services at Netflix. Among many things, it powers the personalized rows you see on the Netflix homepage, and runs at an enormous scale. When we looked at CPU profiles for this service, one feature kept standing out: video serendipity scoring — the logic that answers a simple question:“How different is this new title from what you’ve been watching so far?”This single feature was consuming about 7.5% of total CPU on each node running the service. What started as a simple idea — “just batch the video scoring feature” — turned into a deeper optimization journey. Along the way we introduced batching, re-architected memory layout and tried various libraries to handle the scoring kernels.Read on to learn how we achieved the same serendipity scores, but at a meaningfully lower CPU per request, resulting in a reduced cluster footprint.Problem: The Hotspot in RankerAt a high level, serendipity scoring works like this: A candidate title and each item in a member’s viewing history are represented as embeddings in a vector space...

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