How does the Amazon recommendation system work? – Analyze the algorithm and make a prototype that visualizes the algorithm
I think the recommendation systems are interesting.
I decided that I wanted to learn how the Amazon recommendation works in theory and afterwards implement a demo-site that visualized the algorithm. My implementation is not identical as the Amazon’s version but it follows the same principle. My main focus is to illustrate and visualize the concept. I used a item-to-item matrix table for simplicity.
Reference reading materials:
(2) Amazon Recommendation Patent
I recommend reading (1). This is only 5 pages long and easy to read. Amazon’s algorithm is based on item-to-item filtering. In short: Amazon developed their own recommendation system based on items rather than users, because there are fewer items than users (scalability). The list of visited items by any user is stored in a item-to-item matrix table. The recommendation algorithm are calculated by using the Cosine Similarity function on the vectors from the matrix table.
I recommend reading (2) for technical insights and design overview of how Amazon has implemented it.
Abstract: How does the Amazon recommendation works? This is about visualizing the item to item collaborations filtering mechanism using a item-to-item matrix table. The item-to-item matrix, the vectors and the calculated data values are displayed. There are n different items and the item recommendation can display up to m items. There are implemented different item-to-item neighborhood functions. A simple max count of seen neighbor items, the Cosine Similarity and the Jaccard Index. A tracker keeps track of visited items for any user and is saved to a matrix table. To make it simple only the relation between previous and current viewed item are tracked in this example.
The demo-site has two pages: Home and item page. The item page shows specific information regarding the viewed item and the recommendation is displayed here.
There are 5 different components
- Multiple view - shows multiple components, all the items are displayed here
- User view - shows specific information about the current user in the session
- Item view - shows detailed information about the current item
- Recommendation view - shows recommended items based on the current item
- Data view - visualizes the data structure used by the recommendation algorithm
This shows how the tracker collects the data from the users in to the matrix table. (The illustrated tracking method is a simplified version. You could also iterate the viewed items when a user view a new item to save all the item-to-item relation for the viewed items).
When a new visitor user3 sees the item A,
the recommendation system founds out the closest match are the Items B and C.
The general idea of the Amazon recommendation engine is to locate item vectors which are similar in pattern for the current viewed item vector.
e.g. A vector with pattern [1,1,1,0,0,0,0] is more similar to vector [0,1,1,0,1,0,0] than to vector [0,0,0,1,0,1,1].