ML Case-study Interview Question: Transformer Self-Attention Models for Adaptive E-commerce Recommendations

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Case-Study question

A fast-growing e-commerce company experiences rapidly shifting customer preferences. They observe that customers often browse many products in one category, then suddenly shift to new styles or categories. They want a new recommendation system that can adapt to these changes and transfer learned preferences from one product class to another. They only have access to user browsing history sequences. Design a system that addresses these challenges, improves top-n recall, and leverages minimal data beyond the browse sequence. How would you build this model, and why? Propose the solution, outline the data transformations, model architecture, training approach, and explain how you would evaluate it.

Detailed solution approach

Model Architecture

Transformers are well-suited for sequence modeling of user browse histories. They use self-attention to capture which items in a sequence are most relevant at predicting a user's next product interest. They handle parallel computation effectively, training faster than recurrent methods.

Q, K, and V are the query, key, and value matrices derived from the same input embeddings of the user's browse sequence. d_k is the dimensionality of the key vectors.

The model processes each item in the input sequence, creating item embeddings and adding positional embeddings to reflect the order in which products were viewed. The final output is a vector of scores over all items in the product catalog, ranking them to produce top-n recommendations.

Data Preparation

Browsing data is sorted chronologically. Rare items are filtered out to reduce noise. Consecutive duplicate items are removed because they do not add extra signals. If a user has viewed more than 100 items, only the 100 most recent are kept; if fewer, zero-padding is applied.

Training Details

A binary cross-entropy loss is used to predict whether a user will interact with a given item. A sigmoid function produces a probability for each item. Dropout and L2-regularization mitigate overfitting. Layer normalization and residual connections stabilize gradients and ease training. Learned positional embeddings capture how recent interactions affect current preferences.

Transfer of Learned Preferences

Truncating or zero-padding sequences focuses the model on the most relevant portion of a user’s browsing history. Self-attention highlights recent product transitions, enabling the model to shift recommendations when user style changes. Substitutable classes (for example, multiple sofa types) share strong feature similarities like color or material. Complementary classes (like sofas and coffee tables) share weaker signals, but the model uncovers them through style or aesthetic cues. Subtracting the mean embedding per class isolates style preferences from strong class-specific signals.

Metrics and Performance

Recall at top-n is a key metric. The model is evaluated on whether the recommended items match future purchases. The model should show significant lift over simpler methods such as matrix factorization or top-popular baselines. A 67% lift in recall for top-6 predictions indicates high accuracy gains. Visualizing positional embeddings and item embedding clusters confirms that the transformer is modeling user preference shifts and grouping similar styles together.

Practical Example

Users browsing beds may switch from a traditional design to a modern design. The model sees the recency of the modern product views and re-ranks modern products higher. When users change categories, the embedding space uncovers style consistency across different classes, recommending items with similar aesthetics or price range.

Implementation Snippet

import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerRec(nn.Module):
    def __init__(self, num_items, embed_dim, num_heads, num_layers):
        super().__init__()
        self.item_embedding = nn.Embedding(num_items, embed_dim)
        self.position_embedding = nn.Embedding(100, embed_dim)
        encoder_layers = nn.TransformerEncoderLayer(d_model=embed_dim,
                                                    nhead=num_heads,
                                                    batch_first=True)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
        self.fc = nn.Linear(embed_dim, num_items)

    def forward(self, x):
        seq_len = x.size(1)
        positions = torch.arange(0, seq_len, device=x.device).unsqueeze(0)
        x = self.item_embedding(x) + self.position_embedding(positions)
        x = self.transformer_encoder(x)
        x = x[:, -1, :]  # Use last hidden state
        logits = self.fc(x)
        return logits

No advanced features like residual gating or class-based embedding subtraction are included here, but it demonstrates how to encode item and positional embeddings, feed them through a transformer, and output product scores.

What if the dataset is sparse?

Sparse data arises when many users view few items or vice versa. Transformers can handle zero-padded sequences. L2-regularization, dropout, and restricted vocabulary (filtering rare items) reduce overfitting. Data augmentation strategies or content-based features (such as product images) may help with extremely sparse segments.

How to handle cold-start users or items?

New users lack browsing history. The model might default to popular items or rely on minimal signals. Cold-start items have no interactions. Embedding initialization from related items or using shared metadata helps. Another approach is to incorporate item side information (images, descriptive text) so the model can represent new items.

How to ensure scalability in production?

Transformers are more resource-intensive than simple matrix factorization. Techniques like limiting sequence length to 100 items, adjusting embedding dimensions, and employing multi-GPU distribution can handle large-scale e-commerce catalogs. Batching requests and caching the final hidden states for partial sequences speed up inference.

How to extend the model beyond browsing history?

Use user demographic or product content embeddings (images, text) to enrich item representations. Use multi-modal attention that fuses item IDs with learned representations from images or text. This approach captures style nuances better, improves generalization, and addresses cases where purely behavioral data is insufficient.

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