ML Interview Q Series: Which features would you use for a TikTok recommender using collaborative filtering, and how validate performance?

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Comprehensive Explanation

Collaborative filtering is a popular approach to recommendation systems, leveraging patterns from user-item interactions. It often uses historical user behavior (such as watch history, likes, shares) to predict which content an individual user is likely to find engaging. However, to optimize TikTok’s “For You” page, combining purely collaborative signals with engineered features capturing context, temporal aspects, and user-item characteristics can significantly enhance predictive performance.

Underlying Collaborative Filtering Formula

An essential mathematical representation for matrix-factorization-based collaborative filtering predicts a user’s preference r(u, i) for item i by user u through learned user and item latent factors. The typical formula for the estimated rating or preference is often expressed as

where p_u is the latent factor vector associated with user u in text format, and q_i is the latent factor vector representing item i in text format. Each latent factor vector tries to capture dimensions along which users and items can be aligned (e.g., topics, popularity levels, or content categories).

When dealing with TikTok videos, the concept of an “item” becomes a short-form video clip. We can replace rating with a more relevant implicit signal, such as watch time, skip ratio, or like probability. The objective function typically involves minimizing a loss over these predictions. In the real world, we might use pairwise ranking losses or implicit feedback optimization metrics (e.g., Bayesian Personalized Ranking) to better capture how users prefer certain videos over others.

Important Features and Engineered Features

In a pure collaborative filtering scenario, we might rely on user-video interactions. However, TikTok’s dynamic feed demands more contextual signals:

User engagement signals (collaborative features). This includes watch duration, likes, comments, shares, follows, watch frequency, re-watches, skip frequency, and explicit feedback like “not interested.” These signals are often embedded or aggregated to form numerical features capturing user preference intensity.

User profile features. This might contain user_id as a key, plus user demographics (age bracket, location), language settings, device type, network type, and potential interests gleaned from past behavior.

Video features (item attributes). TikTok videos can have style, genre, hashtags, soundtrack type, language, or length. Visual embeddings from a pretrained CNN or textual embeddings from NLP models (for captions or extracted text) can also provide a dense representation of video content.

Temporal/contextual features. Trending topics or hashtags, time of day, day of week, user session length, or even ephemeral popularity surges. Seasonality can be included to handle daily or weekly patterns of content consumption.

Social graph / user-user relationships. If users follow creators or if friend networks exist, these can be used to adjust recommendations to highlight items from connected creators or from similar communities.

Feature interactions / cross features. Combining user embeddings with item embeddings to get synergy signals. Interactions between user location and item popularity in that region or interactions between device type and video format can reveal deeper patterns.

Testing and Validation Strategy

Offline Evaluation. The most common approach is to split historical data into training and validation sets. For implicit feedback (watch time, skips), we can adopt metrics like recall at K, mean average precision at K, or normalized discounted cumulative gain. This step is used to quickly iterate on model architecture or hyperparameters.

Cross-Validation and Temporal Splitting. Because user preferences shift rapidly, a temporal split (train on older interactions, validate on more recent ones) is more reflective of real-world performance. This helps gauge how quickly the model adapts to new viral content.

Online A/B Testing. In a production scenario like TikTok’s feed, the real test is user engagement. We can roll out the new model to a subset of users. Metrics might include average watch time, retention, ratio of likes/shares, user session duration, or the number of negative signals. Online A/B tests are critical for final acceptance because user interactions in real time can differ drastically from historical patterns.

Shadow Testing. We might run the model silently side-by-side with the current production engine for a period to collect performance metrics without affecting the user experience. If the new model consistently outperforms, we graduate it to an A/B test.

Model Drift Monitoring. TikTok’s content environment changes rapidly, with new videos trending suddenly. Continuous or frequent retraining is critical. We monitor for data distribution shifts (e.g., new classes of content, changes in user demographics) to trigger retraining or recalibration.

Follow-up Questions

What strategies address the cold start problem for new videos or new users?

New videos often lack historical engagement data, making it difficult for standard collaborative filtering to place them accurately in the preference space. Incorporating item content features or user metadata can help. For new videos, content embeddings derived from neural networks analyzing audio tracks, textual captions, or visual frames can fill the gap until real engagement data arrives. For new users, we might rely on demographic or contextual data or prompt them for initial preference inputs (like hashtags they find interesting). Another method is to use short-term interactions (e.g., watch duration on the first few videos shown) to immediately refine the recommendations in real time.

How do we ensure fairness and avoid bias in these recommendations?

Potential biases can creep in via demographic data, popularity bias, or content-based biases. One approach is to incorporate fairness constraints in the optimization objective (for example, ensuring that creators from different regions or demographics receive a fair share of impressions). We can also monitor for distribution differences across subgroups of users or creators, then adjust ranking algorithms or reweight training samples. Techniques like re-ranking or post-processing can help if we detect that certain creators or user segments are systematically underserved or overexposed.

How do you handle ephemeral content trends and rapidly shifting user interests?

Trending content can surge quickly, so a static model might fail to capture these dynamics. Real-time or near real-time updates of item embeddings and popularity signals are crucial. We can implement streaming pipelines that capture user interactions as they happen and feed them into an incremental training setup. Features like the video’s trending status, newly discovered topics, or ephemeral hashtags can be flagged and weighted more heavily in a real-time ranking system. This ensures fresh, trending content is surfaced quickly without discarding the overall personalization.

What advanced techniques could be incorporated to enhance the recommendation engine?

Beyond basic matrix factorization, we can explore neural collaborative filtering architectures such as factorization machines, neural matrix factorization, or sequential models (e.g., Transformers) that capture the order of video consumption. Transformer-based embeddings can capture longer-range dependencies between videos a user watches. Graph neural networks can be introduced if we leverage a user-creator-content graph. We can also integrate reinforcement learning for real-time feedback adaptation, treating user interactions as rewards for specific recommended sequences.

Could you illustrate a minimal viable Python example of collaborative filtering?

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim

# Hypothetical dataset: user_video_matrix with shape (num_users, num_videos)
# user_video_matrix[u, v] = 1 if user u engaged with video v, else 0

num_users, num_videos = 1000, 5000
latent_dim = 50
user_video_matrix = np.random.randint(2, size=(num_users, num_videos))

# Convert to PyTorch tensor
data = torch.FloatTensor(user_video_matrix)

class MatrixFactorization(nn.Module):
    def __init__(self, num_users, num_items, latent_dim):
        super().__init__()
        self.user_factors = nn.Embedding(num_users, latent_dim)
        self.item_factors = nn.Embedding(num_items, latent_dim)
        nn.init.xavier_uniform_(self.user_factors.weight)
        nn.init.xavier_uniform_(self.item_factors.weight)

    def forward(self, user_ids, item_ids):
        u_factors = self.user_factors(user_ids)
        i_factors = self.item_factors(item_ids)
        preds = torch.sum(u_factors * i_factors, dim=1)
        return preds

model = MatrixFactorization(num_users, num_videos, latent_dim)
optimizer = optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.MSELoss()

# Example training loop
for epoch in range(10):
    epoch_loss = 0.0
    # Sample random user-video pairs for training
    user_ids = np.random.randint(0, num_users, size=10000)
    video_ids = np.random.randint(0, num_videos, size=10000)
    targets = data[user_ids, video_ids]

    # Forward pass
    user_ids_torch = torch.LongTensor(user_ids)
    video_ids_torch = torch.LongTensor(video_ids)
    preds = model(user_ids_torch, video_ids_torch)
    loss = loss_fn(preds, targets)

    # Backward pass
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    epoch_loss += loss.item()
    print(f"Epoch {epoch}, Loss: {epoch_loss:.4f}")

This minimal code snippet showcases the core matrix factorization concept: user and item embeddings with a dot product to predict preference. In practice, we’d integrate additional features (e.g., user demographic data, video embeddings, time of watch) to refine predictions. We would also rely on more sophisticated training routines (pairwise loss for implicit data, negative sampling, etc.) and incorporate real-time streaming, feature engineering, and large-scale data infrastructure.

Below are additional follow-up questions

How would you address scalability challenges when dealing with billions of user-video interactions?

A primary concern in large-scale recommendation systems is the exponential growth in data volume. This can overwhelm storage, retrieval, and model training processes.

When scaling to billions of user-video interactions, we often rely on distributed computing frameworks (e.g., Spark, Flink) and specialized data warehouses (e.g., Snowflake, BigQuery). These allow parallel processing of massive datasets. Model training can be distributed via parameter servers or libraries like Horovod (for TensorFlow/PyTorch) to ensure embeddings update in sync across many machines.

A pitfall here is communication overhead in large-scale distributed training. If not carefully designed, gradient synchronization becomes a bottleneck, leading to significantly increased training times. Choosing asynchronous updates or mini-batch strategies for partial updates can mitigate delays. But asynchronous approaches can introduce stale gradient problems, requiring balancing between staleness tolerance and throughput.

Another subtlety involves mini-batch sampling at scale. If user or video distributions become skewed (e.g., some creators generate many videos), the model can overfit to popular items. A remedy is to apply importance-based or stratified sampling to ensure less popular items still appear in training. This must be carefully tuned so that rare user-video pairs are not ignored.

Finally, storing billions of embeddings for users and videos requires memory-efficient data structures, such as hashing or quantization. But using hashing-based embeddings can introduce collisions that degrade performance if not properly sized. Periodic rehashing or pruning of inactive users/videos can maintain system efficiency without seriously impacting performance.

How do you prevent the model from “over-showing” the same viral videos to users repeatedly?

Users can grow fatigued if they see the same top-performing videos too frequently, even if those videos match their historical preferences. Over-recommendation of popular items, known as the “echo chamber” effect, can also limit content diversity.

One approach is to incorporate a diminishing return penalty for videos that a user has already been shown or partially consumed. Practically, a penalty could be a function of how many times or how recently the user encountered a particular video. By modifying the final ranking score with a penalty factor that grows with repetition, the model naturally reduces the probability of re-showing the same clip multiple times.

Another technique is a negative sampling approach that explicitly trains the system to recognize negative user responses over time. If the user skips or only partially watches a video after repeated exposure, these signals are weighted more heavily so that the system quickly learns to down-rank that item for that user.

A real-world edge case occurs when a user initially enjoys a video, replays it, but then quickly loses interest. The model might continue to suggest similar content based on the user’s initial positive signals. This requires capturing short-term feedback loops—perhaps through a session-based or recurrent model that updates user state dynamically as they watch more content, instead of relying solely on historical, aggregated preference signals.

How would you implement user-level personalization across multiple devices or platforms?

Users often consume TikTok content on different devices, such as phones, tablets, or smart TVs. Synchronizing user embeddings or preferences across these devices is vital for consistent personalization.

A common solution is to use a unified user identifier that spans all user devices. The system aggregates interactions and watch history regardless of platform, building a single representation of user interests. In some cases, the user logs in on one device but not on another, leading to partial or anonymous data. One potential pitfall is incorrectly merging user behaviors from multiple people who share the same device. This can lead to noisy or misleading profiles.

To mitigate this, the system can maintain a confidence score for user identity matching. If device usage patterns strongly diverge, the model may keep separate embeddings. Alternatively, we can maintain a hierarchical structure of embeddings—one at the user account level and device-specific child embeddings to capture platform usage nuances (e.g., user might watch longer videos on a tablet).

Additionally, the front-end might adopt dynamic weighting. For instance, smartphone usage might rely on real-time signals like short session watch times or typical commute hours, while the tablet usage profile might highlight longer sessions at home. This multi-faceted approach ensures that across devices, the system retains robust personalization while also respecting environment-specific patterns.

How do you detect and handle malicious behavior, such as bot-driven interactions or spam videos?

TikTok’s massive user base includes potential adversaries who aim to inflate view counts, manipulate trending content, or spam the network. Such behavior skews real interactions, leading to unreliable training data.

One strategy is anomaly detection on user interaction patterns. Automated scripts or bots typically demonstrate unnatural watch times, skip intervals, or repeated patterns that differ from genuine viewers. Clustering or outlier detection methods can flag suspicious accounts. A subtle scenario is semi-human, semi-bot hybrid interactions, which can mimic normal user behavior superficially. To handle this, the system may require advanced ML-based anomaly detection that combines features like interaction timing, device fingerprint, IP address distribution, and concurrency patterns.

Spam content detection relies on analyzing the video content itself—using computer vision or NLP to catch repetitive or low-quality patterns. However, malicious actors might continually adapt. Hence, an ongoing feedback loop is necessary: flagged accounts and videos are re-checked, and the detection model is retrained with fresh examples of malicious behavior.

A real-world pitfall is overzealous filtering that eliminates borderline but legitimate user patterns (e.g., night-shift workers might have unusual usage times). This can degrade user experience. Therefore, thresholds and filtering rules must be carefully tuned, often in consultation with trust and safety teams.

How do you incorporate content moderation policies or platform rules into the recommendation process?

Beyond pure engagement optimization, TikTok enforces community guidelines, such as prohibiting harmful or hateful content. These rules can interact with recommendations in complex ways. Even if a video is highly engaging, it might violate content policies.

A practical method is to assign each video a content safety score or category. Videos flagged as inappropriate or borderline (e.g., containing violent or offensive material) can either be removed from recommendation entirely or significantly down-ranked. The model might incorporate a binary or multi-class feature capturing content compliance status. For repeated policy-offender accounts, the system might degrade or entirely remove their content from personalized feeds.

An edge case arises with borderline content that is not officially disallowed but may cause user discomfort. For instance, sensitive news content or strong language can be acceptable for some but not others. The solution is introducing user preference toggles or an advanced model that recognizes the user’s comfort level. However, balancing user autonomy with platform moderation guidelines is tricky. Over-personalization of borderline content can inadvertently create echo chambers, while under-personalization can suppress user freedom.

A further subtlety is that moderation rules change over time or differ across regions due to cultural and legal variations. The system must adapt quickly, potentially re-scoring historical content. This can cause inconsistencies if a video originally recommended to many users is suddenly deemed inappropriate under revised guidelines. The system must retroactively re-index or remove such content to maintain compliance and user trust.

How do you maintain model performance in the face of sudden user behavior shifts (for example, new privacy policy changes or major world events)?

User behavior can change dramatically due to external factors like global news events, holidays, or privacy policy changes where users suddenly withhold certain data (e.g., location or demographic details). These external shifts can invalidate assumptions embedded in the model’s historical data distribution.

One proactive approach is continuous monitoring of key metrics (like watch time or skip rate) and distribution changes in features (like location usage). If a massive shift is detected—such as a large fraction of users opting out of location data—the system triggers partial retraining or domain adaptation. For example, the model can reweigh or reduce dependence on location-based features to avoid performance degradation.

An additional pitfall is conflating data distribution shift with model underperformance. Sometimes user preferences are stable, but new content trends cause distribution changes. Proper root-cause analysis is crucial: are we seeing a shift in user engagement levels or a novelty wave driven by a viral topic?

Finally, world events can introduce temporary spikes in specific content categories. If not handled, the model could overfit to transient data and degrade user experience once the event concludes. Incorporating a temporal decay factor for certain features ensures that ephemeral spikes do not permanently skew the model’s learned embeddings.