Pinterest interview questions

A two-tower model. The item tower embeds image and text content, so new items get vectors immediately. The user tower runs a transformer over recent actions plus pooled summaries of older history, since 160,000 events won't fit attention.

Build each example as of its timestamp: only earlier events, split train and test by time. For freshness, refresh user embeddings daily and add recent actions at request time. New users fall back to onboarding interests.

Multinomial Naive Bayes over count features, with Laplace smoothing.

import numpy as np

class MultinomialNB:
    def __init__(self, alpha=1.0):
        self.alpha = alpha

    def fit(self, X, y):
        # X (N, V) counts, y (N,) labels
        self.classes = np.unique(y)
        n, V = len(self.classes), X.shape[1]
        self.log_prior = np.empty(n)
        self.log_lik = np.empty((n, V))
        for i, c in enumerate(self.classes):
            Xc = X[y == c]
            self.log_prior[i] = np.log(len(Xc) / len(X))
            cnt = Xc.sum(0) + self.alpha
            self.log_lik[i] = np.log(cnt / cnt.sum())
        return self

    def predict(self, X):
        s = X @ self.log_lik.T + self.log_prior
        return self.classes[s.argmax(1)]

Interviewers check you sum log-probabilities instead of multiplying, which underflows; that smoothing stops one unseen feature zeroing a class; and that you can name the variant to swap in: Gaussian for continuous features, Bernoulli for binary. For probabilities, normalize the scores with log-sum-exp.

A funnel with a latency budget per stage. Retrieval pulls a few thousand candidates from several sources: ANN over item embeddings using a daily long-term user embedding, plus candidates seeded by the user's last few actions. A light pre-ranker trims them; a multi-task ranker predicts save, click and hide, combined into one score; a blender enforces diversity and freshness.

Streaming events update a recent-action sequence in the online feature store within seconds; the ranker reads it alongside long-term features. Log features as served, so training matches serving. Judge by A/B tests.

CTR alone is gameable: clickbait raises it. The auction ranks by bid times predicted CTR, so pCTR must be calibrated, not just well ordered. Offline: log loss, normalized entropy, AUC and calibration by slice. Online: CTR, revenue, advertiser conversions, and hide rate.

Architecture: ad retrieval by targeting and embeddings, then a deep ranker with feature crosses over sparse ID embeddings for user, ad, advertiser and context, with multi-task heads for click and conversion. Correct for position bias. Retrain continuously as ads churn, and recalibrate after each model change.

New ads have no ID history, so predict from what generalizes: creative embeddings, advertiser, category and targeting, with priors from similar ads. Explore with a capped traffic budget and Thompson sampling behind a quality floor.

Hand off by blending both models' scores, shifting weight to the mature model as impressions accumulate. Both must stay calibrated, since the auction uses the score directly. Measure later CTR and the revenue cost of exploring.

Cheapest first. Hash matching against known violating media catches repeats instantly. Then fast classifiers per policy category over fused image and text embeddings, including OCR text, score uploads; heavier models run asynchronously on video frames and borderline items.

A policy layer, not the model, maps scores to actions per category: block, limit distribution, blur, or queue. Thresholds come from precision and recall at each action. The uncertain band goes to human review, prioritized by severity times expected reach; reviewer decisions and appeals become training labels. Track prevalence, precision at each action, and time to action.