1）本文首先利用GRU学习session中item sequence之间的关系（也就是item和item的先后联系），用最后一个item的representation当做用户当前的preference representation
2）本文又通过MF补全了这个评分（或者二分类）的偏好矩阵。
3）利用2）的偏好向量就可以选取1）中间过程产生的item representation了，这些表达同上述的最终的item representation组合到一起。即使本文的要点。这么做的好处，不仅考虑了用户当前偏好（最后一个item），也考虑了item与item之间的关系（GRU的作用），同时利用MF对当前sequence中的item做了提取（考虑long term偏好）
另外，本文也涉及了online Learning的一些设计，主要考虑如下：
1）将历史的部分sessions和当前的session共同组成一个Reservoir用户存储历史信息。这个水库里的session是根据一定的策略，会用最近的session去替换掉历史session。
2）作者认为1）中的水库用来更新模型，样本还是太大。因此用了一个active learning进行采用。主要的采用方法就是根据item的评分，组合成session的评分。这样session就有一个排序。通过这个排序可以进一步构建session被采用的概率。从而形成采用样本，去更新模型，这里所更新的模型其实是本文的子模型（MF）
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Streaming Session-based Recommendation | |||||||||||
| 文献作者 | Lei Guo | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| Session Recommendation; Streaming Recommendation; Attention Model; Matrix Factorization； GRU；paired sample t-test; 配对样本t检验 | |||||||||||
| 摘要描述 | |||||||||||
| Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of user behaviors, and (2) the continuous, large-volume, high-velocity nature of the session data. Recent studies address (1) by exploiting the attention mechanism in Recurrent Neural Network (RNN) to better model the user’s current intent, which leads to promising improvements. However, the proposed attention models are based solely on the current session. Moreover, existing studies only perform SR under static offline settings and none of them explore (2). In this work, we target SSR and propose a Streaming Sessionbased Recommendation Machine (SSRM) to tackle these two challenges. Specifically, to better understand the uncertainty of user behaviors, we propose a Matrix Factorization (MF) based attention model, which improves the commonly used attention mechanism by leveraging the user’s historical interactions. To deal with the largevolume and high-velocity challenge, we introduce a reservoir-based streaming model where an active sampling strategy is proposed to improve the efficiency of model updating. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate the superiority of the SSRM method compared to several state-of-the-art methods in terms of MRR and Recall. | |||||||||||
