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# System prepended metadata

title: 0814 Meeting

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# 0814 Meeting
## Motivation
- SR-GNN等graph based model沒有考慮到
    1. aggregate後的pattern順序性
    2. aggregate時應該考慮幾層鄰居的資訊
- PA-GNN對node embedding加上reverse order embedding來給予attention一些順序上的資訊
![](https://i.imgur.com/8pmM9r9.png)

- **目標: 把所有node embeeding塞進有考慮時間序列的model**
- 類似的pattern會在不同時間上出現
- 2017: iphone X -> airpods 1 -> iphone X cases
  2019: iphone 11 -> airpods pro -> iphone 11 cases

## Problem Settings
- 同個item在不同時間點的session被點擊時應該會具有不同的物理意義
- 考慮不同action的重要性 -> view & click

## Baselines
- A Contextualized Temporal Attention Mechanism for Sequential Recommendation ==缺前處理code==
> https://tianchi.aliyun.com/dataset/dataDetail?dataId=649

![](https://i.imgur.com/XlApncG.png)

![](https://i.imgur.com/64CMIwr.png)

- ==**Make It a Chorus: Knowledge- and Time-aware Item Modeling for Sequential Recommendation**==
![](https://i.imgur.com/9MnPyQh.png)

![](https://i.imgur.com/DsoP8tx.png)

![](https://i.imgur.com/owTkbZk.png)

- Time Interval Aware Self-Attention for Sequential Recommendation
    - 對position和time interval做embedding
    - 換成session資料 => jdata優先, 因為時間範圍也很廣

![](https://i.imgur.com/6QJxJcZ.png)

![](https://i.imgur.com/vmbuGxE.png)

- Time Matters: Sequential Recommendation with Complex Temporal Information
    -  Absolute Time Pattern
        - customers are more likely to buy T-shirts in summer than in winter
    -  ==Relative Time Pattern==
        - focuses on the **time interval between each pair of user behaviors**
        - The green line presents a user’s repeated purchases of guitar picks
        - guitar->tuner (red line): Most users purchase guitars and tuner together
        - guitar->strings (blue line):  buy new strings about 150 days later than the guitar

![](https://i.imgur.com/dX04Zoc.png)

- Global Context Enhanced Graph Neural Networks for Session-based Recommendation

![](https://i.imgur.com/yXJV73I.png)
![](https://dl.airtable.com/.attachmentThumbnails/543c58337b293816ecad2fbdb875daee/810a158f)

:::spoiler
![](https://i.imgur.com/lHGpnJk.png)
:::

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:::spoiler
- DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks
![](https://i.imgur.com/PzDIlo2.png)

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- GACOforRec: Session-Based Graph Convolutional Neural Networks Recommendation Model 
![](https://i.imgur.com/Y3rsWzE.png)

- Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems
![](https://i.imgur.com/7IlG5RV.png)

:::

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## Experiments
> https://docs.google.com/spreadsheets/d/1zt0VoYEde4HbgbkTIJVProU2dTSEA0xDXtnyDM3aRA4/edit?usp=sharing

![](https://i.imgur.com/LHrt1VX.png)

- jdata, Tabaoo, Tmall, yochoose 1/4待補
- kkbox沒有timestamp

> yoochoose 1/4 跑1 epoch大該需train 7小時
> ![](https://i.imgur.com/EPDzmSP.png)

其他篇論文給的數據
![](https://i.imgur.com/jxdCoIV.png)


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某區user
A區關注iphone8 -> 換B區關注 ->換C區
隔年, A區關注iphone11 -> 換B區關注 ->換C區

- 上方改成dynamic graph (放入user/ session id), 讓不同時間的item有不同embedding, 最後和原本的session embedding結合
