Physical Meaning Behind Matrix Factorization

As we all know, Matrix Factorization is an effective method to do rating prediction jobs in recommender systems. Thanks to the work of Yahuda Koren. My question is why MF can do this job? What's the physical meaning behind it?

The idea in matrix factorization is to find the latent variables which connect the input and the output. Suppose that we are interested in a movie recommendation system, and that movies "live" on a one-dimensional axis, having romantic comedies in one end, and action movies in the other. Each "input" and "output" move can be rated in this scale (say $1$ to $-1$). Given the input movies, we can estimate a person's location $p$ on the axis (say a point in $[-1,1]$) by taking the inner product between the movies they saw and their "ratings" $r_i$, i.e. $p \approx \frac{1}{n} \sum_{i=1}^n r_i$, where $1,\ldots,n$ are the movies seen by the person. Given the location, we can estimate how much that person will like other movies: movie $t$ will be liked by an amount proportional to $pr_t$ (this is just a simplistic linear model).

In this example, the recommendation matrix can be decomposed as an outer product $A_{it} = r_i r_t$. More generally, the situation could depend on several latent variables, and this is the general situation described by matrix factorization. The number of latent variables is the "middle" dimension in the factorization. If you have found a good factorization with only a few variables, then you have a succinct explanation of your data that could have predictive power.

StackExchange Computer Science Q#22609, answer score: 7