改进负采样中的排名估计，提升推荐系统训练效率与准确率
Improved Estimation of Ranks for Learning Item Recommenders with Negative Sampling
- 提出修正负采样引入的排名偏差的新方法
- 在WARP和LambdaRank上验证，显著提升推荐质量
- 适合大规模推荐系统优化与负采样研究者
在推荐系统中，可推荐项目数量（如电影、音乐、商品）持续增长，导致模型训练与评估计算成本高昂。为降低开销，常采用负采样策略。然而传统负采样机制会引入偏差，影响推荐质量。本文证明纠正负采样带来的偏差具有显著优势。首先，我们推导了经典方法WARP和LambdaRank的批处理负采样版本；其次，提出通过改进排名估计来提升性能；最后，实验证明结合所提校正技术后，即使使用负采样，WARP与LambdaRank仍能高效学习并获得更优推荐效果。
原文摘要 · Abstract (English)
In recommendation systems, there has been a growth in the number of recommendable items (# of movies, music, products). When the set of recommendable items is large, training and evaluation of item recommendation models becomes computationally expensive. To lower this cost, it has become common to sample negative items. However, the recommendation quality can suffer from biases introduced by traditional negative sampling mechanisms. In this work, we demonstrate the benefits from correcting the bias introduced by sampling of negatives. We first provide sampled batch version of the well-studied WARP and LambdaRank methods. Then, we present how these methods can benefit from improved ranking estimates. Finally, we evaluate the recommendation quality as a result of correcting rank estimates and demonstrate that WARP and LambdaRank can be learned efficiently with negative sampling and our proposed correction technique.
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