Howtosignalprocessing
- Why use matrix factorization in recommendation system?
- Does Netflix still use matrix factorization?
- Does matrix estimation provide personalized recommendations?
- Why use NMF over SVD?
Why use matrix factorization in recommendation system?
Collaborative filtering is the application of matrix factorization to identify the relationship between items' and users' entities. With the input of users' ratings on the shop items, we would like to predict how the users would rate the items so the users can get the recommendation based on the prediction.
Does Netflix still use matrix factorization?
Latent matrix factorisation was shown to outperform other recommendation methods in the Netflix Recommendation contest and has become hugely popular ever since. Matrix factorisation can be extended to more complex models through deep learning, where the user-item matrix is decomposed into many layers.
Does matrix estimation provide personalized recommendations?
In return, the collaborative filtering system provides useful personalized recommendations for new items.
Why use NMF over SVD?
In such cases NMF works better as the missing-values assumption is inbuilt to the algo. In case of SVD, it doesn't assume anything about missing values. So you need to give some missing value imputation for SVD. This might bring in unnecessary noise.
