4. Matrix Factorization

The reference article emphasizes matrix factorization variants. This remains foundational for data scientists.

4.1 PMF / latent factors (explicit feedback)

Model:

$${\hat{r}}_{ui}=p_u^{\mathrm{\top }}{q}_i$$

where user and item embeddings $p_u,q_i\in {\mathbb{R}}^f$.

Regularized loss over observed pairs $\mathrm{\Omega }$:

$$\begin{array}{rlr}\underset{P,Q}{min}& \sum _{(u,i)\in \mathrm{\Omega }}{(r_{ui}-p_u^{\mathrm{\top }}{q}_i)}^2& +\lambda (\Vert p_u{\Vert }_2^2+\Vert q_i{\Vert }_2^2)\end{array}$$

Optimization:

• SGD (simple, flexible)
• ALS (efficient for large sparse systems)
• Practical implementations are available in the Surprise library and its documentation

With ALS, you alternate between solving for user factors while holding item factors fixed and solving for item factors while holding user factors fixed. That makes large sparse factorization problems easier to optimize in practice.

Image credit: Dive into Deep Learning, CC BY-SA 4.0.

4.2 SVD-style bias terms

A common extension adds global/user/item bias terms:

$${\hat{r}}_{ui}=\mu +b_u+b_i+p_u^{\mathrm{\top }}{q}_i$$

Biases capture broad effects (strict users, broadly popular items) and usually improve quality.

4.3 Implicit-feedback factorization

Following the article’s logic, implicit events are treated as preference plus confidence.

One common setup:

• Preference: $p_{ui}\in 0,1$ from interaction presence
• Confidence: $c_{ui}=1+\alpha \cdot t_{ui}$, where $t_{ui}$ is interaction strength

Objective:

$$\begin{array}{rlr}\underset{X,Y}{min}& \sum _{u,i}{c}_{ui}{(p_{ui}-x_u^{\mathrm{\top }}{y}_i)}^2& +\lambda (\Vert x_u{\Vert }_2^2+\Vert y_i{\Vert }_2^2)\end{array}$$

This is the core weighted-implicit matrix factorization approach used in large-scale recommenders.

The Google course adds an important weighted-matrix-factorization view that is especially useful in industrial retrieval systems. Let $A$ be the feedback matrix and let $\mathrm{obs}$ denote observed interactions. A common weighted objective is:

$$\begin{array}{rlr}\underset{U,V}{min}& \sum _{(u,i)\in \mathrm{obs}}{(A_{ui}-⟨U_u,V_i⟩)}^2& +w_0\sum _{(u,i)\notin \mathrm{obs}}⟨U_u,V_i{⟩}^2\end{array}$$

Here $w_0$ controls how strongly the model treats unobserved pairs as weak negatives. In practice, this matters a lot: too little weight on unobserved pairs can make the embedding space collapse, while too much weight can wash out true positives. Google also notes that frequent users or popular items can dominate the objective, so observed pairs are often reweighted by user or item frequency.

4.4 Evaluation for rating prediction

For explicit-feedback recommendation, D2L’s matrix factorization section uses RMSE as the primary evaluation measure:

$$\mathrm{RMSE}=\sqrt{\frac{1}{|\mathcal{T}|}\sum _{(u,i)\in \mathcal{T}}{(r_{ui}-{\hat{r}}_{ui})}^2}$$

where $\mathcal{T}$ is the evaluation set of observed user-item pairs.

RMSE is appropriate for rating prediction, but it is not sufficient for top-$n$ recommendation because it does not evaluate rank order.

4.5 AutoRec for nonlinear rating prediction

AutoRec extends collaborative filtering with an autoencoder-style reconstruction objective.

• Input is a partially observed user vector or item vector from the rating matrix
• The network reconstructs missing entries through a hidden representation
• Only observed ratings should contribute to the training loss

For item-based AutoRec, D2L writes the input as the $i$th column $R_{\ast i}$ of the rating matrix and reconstructs it with a nonlinear network:

$$h(R_{\ast i})=f(W,g(V{R}_{\ast i}+\mu )+b)$$

The learning objective minimizes reconstruction error over observed entries only:

$$\begin{array}{rlr}\underset{W,V,\mu ,b}{min}& \sum _{i=1}^M{\Vert R_{\ast i}-h(R_{\ast i})\Vert }_{\mathcal{O}}^2& +\lambda (\Vert W{\Vert }_F^2+\Vert V{\Vert }_F^2)\end{array}$$

Conceptually, AutoRec matters because it is one of the earliest examples in D2L of moving from linear collaborative filtering to nonlinear neural reconstruction for rating prediction.

4.6 Personalized ranking objectives

D2L makes an important distinction between rating prediction objectives and ranking objectives.

• Pointwise objectives model one user-item interaction at a time
• Pairwise objectives model relative preference between a positive and a negative item
• Listwise objectives optimize properties of an entire ranked list

For top-$n$ recommendation from implicit feedback, pairwise objectives are often a better match to the task.

The two core D2L losses are:

1. Bayesian Personalized Ranking (BPR), which encourages the positive item to score above a sampled negative item:

$$\sum _{(u,i,j)\in D}\ln \sigma ({\hat{y}}_{ui}-{\hat{y}}_{uj})-{\lambda }_{\mathrm{\Theta }}\Vert \mathrm{\Theta }{\Vert }^2$$

2. Hinge ranking loss, which pushes the positive item away from the negative item by a margin $m$:

$$\sum _{(u,i,j)\in D}max(m-{\hat{y}}_{ui}+{\hat{y}}_{uj},0)$$

These are central for implicit-feedback recommendation because they optimize relative ordering rather than absolute score accuracy.

4.7 SVD++ intuition

SVD++ augments user representation with signals from interacted items, helping when explicit feedback is sparse but interaction history exists.