Recommender systems implementation detail

Mean normalization

TensorFlow implementation of collaborative filtering

• Derivatives
• Gradient Descent

Finding related items

• The features $x^{(i)}$ of item $i$ are quite hard to interpret
• To find other items related to it, find item $k$ with $x^{(k)}$ similar to $x^{(i)}$
  • ${\sum }_{l=1}^n(x_l^{(k)}-x_l^{(i)}{)}^2$
  • $||x^{(k)}-x^{(i)}|{|}^2$
  • ex) with smallest distance

Limitations of Collaborative Filtering

• Cold start problem. How to
  • rank new items that few users have rated?
  • show something reasonable to new users who have rated few items?
• Use side information about items or users:
  • Item: Genre, movie stars, studio, …
  • User: Demographics (age, gender, location), expressed preferences, …