 We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store items (and reuse the computed representations ) from previous batches, where the queue serves as the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
<coherence>  We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store items (and reuse the computed representations ) from previous batches, where the queue serves as the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
 We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.