题目中的Sequential并不是核心，感觉没怎么在文章中体现？指的是sequential learning？
Scenario-Specific倒是充分利用了，如不同类，不同tag都可以是不同的scenario
我觉得本文主要关心的问题：当样本比较少的时候，通常的SGD中必有的几个元素（初始值，learning ratio，early-stop policy）可能不适用于few-shot learning。原因是过拟合。
所以本文提出了针对同时拥有多个scenario情况下，如何从上面3个元素出发，解决这个过拟合问题。
1）初始值。不同scenario共用一套初始值（共同学习和更新），这样至少可以保证在初始值这块能够保证单个scenario的样本不那么少。
2）learning ratio。传统的可能设置搜索区域。而本文采用的是这个learning ratio可以放到LSTM里去更新和调整，作为模型参数（scenario-specific）。
3）early-stop policy。同理，这个policy关联一个stop概率，这个概率也是放到LSTM里去调整和学习，然后决定什么时候去stop参数的更新。
最后通过调整好的参数，去更新初始值等。2）和3）的结合，避免了像传统方法那样，一条路学到死，学出一个过拟合的模型。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Sequential Scenario-Specific Meta Learner for Online Recommendation | |||||||||||
| 文献作者 | Zhengxiao Du | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| few-shot learning; meta learning; LSTM; early-stop policy; learning rate; | |||||||||||
| 摘要描述 | |||||||||||
| Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems using few-shot learning and meta learning. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. To accomplish this, we combine the scenario-specific learning with a model-agnostic sequential meta-learning and unify them into an integrated end-toend framework, namely Scenario-specific Sequential Meta learner (or s2Meta ). By doing so, our meta-learner produces a generic initial model through aggregating contextual information from a variety of prediction tasks while effectively adapting to specific tasks by leveraging learning-to-learn knowledge. Extensive experiments on various real-world datasets demonstrate that our proposed model can achieve significant gains over the state-of-the-arts for cold-start problems in online recommendation1. Deployment is at the Guess You Like session, the front page of the Mobile Taobao; and the illustration video can also be watched from the link2. | |||||||||||
