---
# System prepended metadata

title: 12-30 Meeting
tags: [meeting]

---

---
tags: meeting
---

# 12-30 Meeting
## 新版GraphSAGE
Training loss
<!-- ![](https://i.imgur.com/oqevCnG.png)

各自feature normalize -->
![](https://i.imgur.com/opBVbGQ.png)

<!-- ![](https://i.imgur.com/GIkxlkU.png)

![](https://i.imgur.com/Xt28dYZ.png) -->


Training MAP
推薦從knn改成dot算score, 且只挑references超過20篇
![](https://i.imgur.com/iAfsTMi.png)
**要比對一下推薦的部分哪裡有問題**

```python
graph_scores = np.dot(paper_emb_all, paper_emb.T)
graph_recommend_papers = y_all[np.argsort(-graph_scores)][1:K+1]
```

* MAP@150
<!-- ![](https://i.imgur.com/6Vqg4s2.png) -->
![](https://i.imgur.com/GzuFz6R.png)

測試一下的Graph Embedding做推薦也有相同問題
![](https://i.imgur.com/DffJV3p.png)

目前推測是在算Dot做推薦時有問題，接下來要檢查以下:
- MAP的Answer有沒有錯
- 推薦的部分是不是有問題

<!-- 沒答案的會讓MAP是1.0
![](https://i.imgur.com/yUiVyja.png)

![](https://i.imgur.com/tTUJ9Ze.png) -->


## Optimize Model
## LINE
### First Order and Second Order
![](https://i.imgur.com/WcGMd7O.png)
### BEACON
![](https://i.imgur.com/Sk7Du60.png)
![](https://i.imgur.com/t4BIQac.png)
* Author: 800/98001 
* Correlation Matrix: (16065, 16065)
* Result:
![](https://i.imgur.com/LrlF3M1.png)


