Recommendation Systems : Approaches

There are several approaches in building up a recommendation system. I have been intrigued at how we can connect the different approaches, and understand the pros and cons of each approach.

Here is a high-level overview of the approaches being used for solving the recommendation problem:

[1] High Level Approaches:

• Content Based
  • Based on using weights across content features
• Collaborative Methods
  • Based on an approach : “users who liked this also liked”

[2] Collaborative Filtering:

• Memory Based (e.g. K-nearest neighbors)
• Model Based (e.g. Matrix Factorization)

[3]  Collaborative Filtering : Memory Based : K-nearest neighbors

• Key Intuition: “Take a local popularity vote among “similar” users”
• Need to quantify similarity, predict unseen rating.
• Can take 2  forms :
  • Item-item collaborative filtering, or item-based, or item-to-item
  • User-User collaborative filtering

[4]  Collaborative Filtering : Model Based : Matrix Factorization.

• Key Intuition : Model item attributes as belonging to a set of unobserved topics
  and user preferences across these topics

• Model quality of fit with squared-loss.

• There are 2 ways to do the loss optimization :
  • Alternating Least Squares.
  • Stochastic Gradient Descent.

 

References:

1. https://en.wikipedia.org/wiki/Item-item_collaborative_filtering
2. http://dl.acm.org/citation.cfm?id=372071