The success of "infinite-inventory" retailers such as Amazon.com and Netflix
has been largely attributed to a "long tail" phenomenon. Although the majority
of their inventory is not in high demand, these niche products, unavailable at
limited-inventory competitors, generate a significa
本文提出了基于互补学习和课程学习的新框架 Mutual Enhancement of Long-Tailed user and item (MELT)，它是序列推荐系统中第一篇同时解决长尾用户和物品的问题，同时也是模型无关的。实验结果表明，该方法可以有效地缓解长尾问题，并且不会牺牲头部用户和物品的性能。