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# System prepended metadata

title: Untitled

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1. Architecture of the recommender system 
    - Item feature extraction
       - photos, videos and news
    - User feature/relation extraction
    - Online/Offline 召回
       - Trade-off between accuracy and latency
    - 粗排，精排
    - Graph embedding for knowledge graph? social graph?

2. Metrics 
    - Pupular/practical offline metrics (e.g. AUC, recall and precision, and the weighted sum of them)
    - Online metrics (用户留存, A/B testing results?)
    - Practical procedure (offline metrics as experiments, online test for final decision?)

3. Training 
    - Training on unbalanced data (正例很少，如何扩充)
    - Sampling for speedup and unbalance data

4. Uncerntainty estimation
    - help on cold-start