# Mathematical Notation for Recommender Systems

Over the years of teaching and research, I have gradually standardized the notation that I use for describing the math of recommender systems. This is the notation that I use in my classes, Joe Konstan and I have adopted for our MOOC, and that I use in most of my research papers. (And thanks to Joe for helping revise it to its current form.)

If you haven’t already settled on a notation, perhaps you would consider adopting this one. I also welcome feedback on improving it.

I have tried to strike a balance between clarity and clutter. I slightly overload the meaning of some symbols; in particular, I am loose with distinctions between sets and matrices, because it is generally clear from context which is being invoked; I do not overload external referents, however. I also have tried to keep this notation so that it can be hand-written, making it more useful in teaching but meaning that I cannot rely as much on typography to distinguish different objects (e.g. separating $$U$$ and $$\mathcal{U}$$ would be questionable).

## Input Data

Our input data, for collaborative filtering, consists of:

$$U$$
The set of users in the system or data set.
$$I$$
The set of items in the system or data set.
$$R$$
The set of ratings in the data set. $$R$$ can be used as either a set of rating observations or as a (partially observed) $$|U| \times |I|$$ matrix; context makes it clear which meaning is intended.

Within each of these sets, we can refer to individual entries:

$$u, v \in U$$
I use $$u$$ and $$v$$ as variables referencing individual users. If I need more than two, then I use numeric subscripts $$u_1$$, $$u_2$$, etc.
$$i, j \in I$$
I use $$i$$ and $$j$$ as variables referencing individual items. Again, for more than two, I use numeric subscripts. This does mean that $$i$$ is not available as a counter variable, but I do not find that to be too much of a difficulty in practice.
$$r_{ui} \in R$$
An individual rating value, the rating user $$u$$ gave for item $$i$$. In an implicit feedback setting, this takes on whatever value you are using as the ‘rating’: 1/0, a play count, etc.

One advantage of always using $$u,v$$ for users and $$i,j$$ for items is that meaning is clear from looking at a variable or subscripted variable. It also allows the following subset notations:

$$I_u \subset I$$
The set of items rated by user $$u$$.
$$U_i \subset U$$
The set of users who have rated or purchased item $$i$$.
$$R_u \subset R$$
The set of ratings given by user $$u$$
$$R_i \subset R$$
The set of items for item $$i$$
$$\vec{r}_u$$
User $$u$$’s rating vector, an $$|I|$$-dimensional vector with missing values for unrated items.
$$\vec{r}_i$$
Item $$i$$’s rating vector, a $$|U|$$-dimensional vector with missing values for users who have not rated the item.

## Scores and Similarities

With this notation, we can write things like the user-user rating prediction formula:

$\hat r_{ui} = s(i;u) = \frac{\sum_{v \in N(u,i)} w(u,v)(r_{vi}-\bar r_v)}{\sum_{v \in N(u,i)} |w(u,v)|} + \bar r_u$

$$N(u;i)$$ is the neighborhood for user $$u$$ for the purpose of scoring item $$i$$, and $$\bar r_u$$ is user $$u$$’s average rating. I explicitly state the ranges of my summations for two reasons, even if they are implicitly clear for experienced recsys researchers: it is a common tripping point for students, and it is a place where subtle implementation differences get buried. I find that it is not overly cumbersome with this notation.

## Matrix Factorization

For basic decomposition of the ratings matrix, I usually use $$P$$ and $$Q$$ these days:

$R \approx PQ^{\mathrm{T}}$

We then have user vectors $$\vec p_u$$ and item vectors $$\vec q_i$$. I like using the transpose notation for the factorization, so that latent features are on the columns of both $$P$$ and $$Q$$.

Advanced decompositions that involve more than two matrices will need to find additional matrices; one unfortunate tradeoff of using $$U$$ for the set of users is that it is no longer available for a matrix.