The 3 types
- Item-Item Content Filtering: “If you liked this item, you might also like …”
- Item-Item Collaborative Filtering: “Customers who liked this item also liked …”
- User-Item Collaborative Filtering: “Customers who are similar to you also liked …”
There are other trivial domain dependent (like most-popular / trending today). They use global data (or global-data per category) and do not distinguish between items
Item-Item Content Filtering: If you liked this item, you might also like …”
How: Find similarity between items. Finding similarity is an art of itself, but essentially it's a two steps procedure: cut-into-(good)-features and calculate-distance-using-features. Lately deep-learning is doing both together.
Example: Pandora processes a song into it's features (rock/classic, fast/slow) and finds similar songs.
For books, you can look into book-genre + author-name
For books, you can look into book-genre + author-name
For fashion photos, you can (but it's hard) use deep-learning to find similar dress/shoe style
Item-Item Collaborative Filtering: “Customers who liked this item also liked …”
Example: Amazon (at least the first years)
How: Use a lot of user data. Good for companies with big user data and not for new-comers.
Market-basket based: If you have purchase-history you just count at the most common items purchased together (for cat-food: cat-toy=50% , cat-litter=40%, dog-food=0.3% etc). Drawback of this approach is that we use one purchase items and not the whole history of the user, and that we know he bought it, but don't know if he liked it in the end or returned it.
Rating based: If you also have enough user-rating(stars), you can look at how much users ranked what, for example: those giving harry-potter 1 '5 stars' also gave harry-potter 2 '5 stars' average, but gave dan-brown '1 star' ,
User-Item Collaborative Filtering: “Customers who are similar to you also liked …”
Example: Netflix challenge
Find similarity between users according to the rating they gave to the same movies, but do that after normalization (some users love-everything with mean score of 4 of 5, some are "haters" with 2 of 5 mean average, so the first giving 3, is like the hater giving 1).
Implementation using NN:
For large data-sets , this work best. It can be implemented in rather small Keras NN.
In few words, find latent vector of users and movies.
Linear: The rating should be the dot-product of a user latent-vector and movie-vector. Optimize to minimize diffs.
Better, non-linear : The rating will be the result of a shallow NN. optimize to minimize diffs again.
User-User something:
Suppose you know the user from a different website, you are Audioable which want to recommend books to buy, but you are also part of Amazon and you know the (unrelated to books) Amazon site browesing history. Can it help ?
You can model users as similar to users from a different perspective and use this info for the cold-start problem.
Implementation using NN:
For large data-sets , this work best. It can be implemented in rather small Keras NN.
In few words, find latent vector of users and movies.
Linear: The rating should be the dot-product of a user latent-vector and movie-vector. Optimize to minimize diffs.
Better, non-linear : The rating will be the result of a shallow NN. optimize to minimize diffs again.
User-User something:
Suppose you know the user from a different website, you are Audioable which want to recommend books to buy, but you are also part of Amazon and you know the (unrelated to books) Amazon site browesing history. Can it help ?
You can model users as similar to users from a different perspective and use this info for the cold-start problem.
Appendix
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