 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.
<meaning-changed>  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 items (and reuse the computed representations ) from previous batches, where the queue serves as the items' representations computed in previously processed batches, and use the queue to serve as an effective sampler of negative examples. This queue-based design provides great efficiency in incorporating rich features of the thousand negative items per batch thanks to computation reuse . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.