ML Case-study Interview Question: Enhancing Sponsored Search Ranking with Short-Term Transformer-Based User Embeddings

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Case-Study question

A major eCommerce platform has a sponsored search system that displays paid listings alongside organic search results. They want to incorporate a short-term personalization signal using a one-hour window of user actions to better predict key metrics like clickthrough rate and post-click conversion rate. They record user actions such as item views, favorites, add-to-cart events, and purchases within the last hour. They also track search queries and categories visited. They have limited data for many sessions (sometimes only a few items viewed), but they still want to build a robust personalization module that can generalize to both logged-in and logged-out users. They aim to encode these recent actions into a condensed user representation, then feed it into a ranking model that predicts how likely a user is to click or convert on each listing.

Describe a comprehensive strategy to achieve this personalization. Propose an approach for how to represent and aggregate these short-term events. Discuss how to integrate pretrained item representations (e.g. image, text, multimodal) and how to learn new representations in real time when pretrained embeddings are unavailable. Specify the neural architecture components, how they might be combined, and how you would incorporate them in a downstream ranking model. Provide ideas on how you would evaluate the impact of your design, both offline and in an online A/B setting. Address any potential pitfalls such as data sparsity, latency constraints, and distribution shifts.

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Detailed Solution

Overall Module: ADPM

This solution uses a three-component deep learning module that encodes a user’s short-term behavior in the last hour. The module is referred to as an ADPM. It ingests sequences of recent user actions, processes them, then outputs a personalized embedding for the user’s current session. This embedding is appended to the inputs of a ranking model that estimates clickthrough or conversion probability.

Component One: adSformer Encoder

This first component applies a custom transformer block that begins by embedding each user action in the one-hour sequence. Each action is associated with an item ID or a query or a category, plus a positional index. A multi-head self-attention mechanism captures contextual dependencies. A feed-forward layer with a nonlinear activation refines the output. Instead of returning the entire attention output, the system applies a global max pooling to extract the most salient features.

x is the hidden representation at each step. MHSA is multi-head self-attention. FFN is a feed-forward network with a LeakyReLU activation. o1 is the final pooled vector representing the sequence.

Component Two: Pretrained Representations

A second component encodes items using embeddings learned offline via image, text, or multimodal training. Each item has a fixed embedding, which may be computed through a multitask image classification model or a metric-learning representation. These embeddings are retrieved in real time and combined by global average pooling over all item embeddings in the last hour.

Here e_j are d-dimensional pretrained embeddings for the jth item in the one-hour window.

Component Three: On-the-Fly Representations

A third component learns new embeddings for certain entity-action pairs (e.g. shop IDs or other signals) when no pretrained representation exists. These embeddings are trained concurrently with the downstream task. They are aggregated by global average pooling across all relevant entities, then concatenated into a single vector.

z can be multiple different types of entity-action sequences within the last hour.

Final Aggregation and Usage in Ranking

All three outputs are concatenated to form the user’s short-term representation:

This u is appended to the input of the main ranking model. The model might be a deep neural network that contains layers for cross-feature interactions and a multilayer perceptron. The ranker then generates a probability of click or conversion for each candidate listing.

Implementation Aspects

Engineers typically implement the module in TensorFlow or PyTorch as a reusable sub-network. They stream the user’s actions in real time and pass them into the module in reverse chronological order to capture the most recent activities first. A final global pooling step guarantees the module can handle variable-length sequences.

A feasible code skeleton in Python (pseudo-style) for building the ADPM might look like this:

import tensorflow as tf

class ADPM(tf.keras.Model):
    def __init__(self, pretrained_item_dim, on_the_fly_dim, max_seq_length):
        super().__init__()
        self.embedding_layer = tf.keras.layers.Embedding(
            input_dim=some_vocab_size,
            output_dim=some_embed_dim
        )
        self.transformer_block = CustomTransformerBlock(...)
        self.global_max_pool = tf.keras.layers.GlobalMaxPooling1D()
        self.global_avg_pool = tf.keras.layers.GlobalAveragePooling1D()
        self.on_the_fly_embedding = tf.keras.layers.Embedding(
            input_dim=some_other_size,
            output_dim=on_the_fly_dim
        )
        self.max_seq_length = max_seq_length

    def call(self, item_id_sequence, on_the_fly_sequence, pretrained_vecs):
        x = self.embedding_layer(item_id_sequence)
        x = self.transformer_block(x)
        o1 = self.global_max_pool(x)
        o2 = self.global_avg_pool(pretrained_vecs)
        ofe = self.on_the_fly_embedding(on_the_fly_sequence)
        o3 = tf.concat([self.global_avg_pool(ofe_i)
                        for ofe_i in tf.split(ofe, self.split_config, axis=1)], axis=1)
        return tf.concat([o1, o2, o3], axis=1)

Once the ADPM outputs a vector for the user’s session, that vector is concatenated with other contextual features and passed to a deep-and-cross network or other architecture.

Evaluation and Deployment

An offline evaluation often measures AUC or log loss differences when comparing the new personalization module to a baseline. A subsequent online A/B test measures improvement in clicks, conversions, or other downstream metrics. Because the module depends on real-time data, the system needs a reliable streaming pipeline to ensure user actions are processed quickly.

Handling Data Sparsity and Distribution Shifts

Many short sessions contain few actions, so the architecture uses pretrained embeddings to inject rich semantic signals from items. The on-the-fly embeddings and attention mechanism can also generalize from partial contexts. A global pooling avoids sensitivity to sequence length. Short sessions remain representable through whichever few actions occur. Distribution shifts may arise when new item categories appear, so the pretrained embeddings are updated periodically, and the on-the-fly embeddings adapt during downstream training.

Potential Follow-up Questions

1) How would you handle sessions that have almost no user actions?

One approach is to reduce reliance on the adSformer encoder’s attention outputs when the input sequence is empty or nearly empty. The module can short-circuit the transformer path and rely more on pretrained or default embeddings for the user. The final pooling layers still output a default representation. You can also backfill context from other signals like top-level categories visited or partial user history if available.

2) How do you train the component for on-the-fly representations so that it does not explode in size when there are many possible entities?

A possible solution is to share an embedding table across all potential entity IDs, then rely on sub-IDs or hashing to handle massive cardinalities. You may also restrict on-the-fly learning to the most frequently occurring entities or limit how many embedding parameters are allocated for rarely-seen entities. An L2 regularization or embedding dropout can help keep this component stable.

3) Why not rely solely on the transformer encoder without pretrained embeddings?

Pure transformers can work, but might not capture robust image or text semantics when user actions are limited. Pretrained embeddings incorporate offline learning from large datasets, thus enriching the representation. Transformers alone can overfit quickly in sparse sessions, missing the rich semantic context that pretrained embeddings provide.

4) How would you ensure low latency in retrieving pretrained embeddings for each item?

One approach is to store the pretrained embeddings in a fast in-memory key-value store. When the user views an item, the system quickly fetches the corresponding vector. You can also pre-batch embedding lookups for multiple items at once. Caching the embeddings in local memory of your inference service helps reduce repeated lookups.

5) What if items contain multiple images, categories, or textual attributes?

An approach is to combine or fuse multiple representations into one vector per item. You can average multiple image embeddings or feed them into a small aggregator network. For textual attributes, you can either concatenate them and feed them into a single text encoder or pool across multiple textual fields. This aggregator step is usually performed offline, resulting in a unified embedding for each item ID.

6) Why not just store a fixed one-hour user embedding and pass it to the model, rather than using a transformer-based encoder at inference?

A fixed embedding might miss the significance of how events are ordered. The transformer-based encoder sees the sequence structure, letting the system learn patterns like repeated queries or incremental refinement. A strictly average-based or fixed approach might underutilize the sequence’s temporal dimension. The adSformer block’s ability to capture attention-based interactions is often critical when session actions reveal subtle changes in intent.

7) How do you mitigate catastrophic forgetting in the transformer’s attention and the on-the-fly embeddings, given that user interest can shift quickly?

A technique is to limit sequence length to one hour, then rely on a smaller window so that older context does not override new signals. It also helps to combine short-term signals with longer-term user features if the user is logged in. Regularization and gating mechanisms can prevent the new signals from wiping out well-learned representations.

8) How do you transition from a research prototype to serving this model at scale?

You would integrate it in a production environment with a scalable model hosting infrastructure, possibly with TensorFlow Serving or a similar system. Real-time logs must push user actions into a feature store or streaming system. The embedding tables and ADPM code can be deployed in containers or microservices behind an online inference API. Load tests ensure that the infrastructure can handle latency requirements.

9) How would you debug performance issues if the new module fails to improve online metrics?

You would segment sessions by length, device type, or item categories. You might investigate the ADPM outputs to see if they are saturating or if the pretrained embeddings are not capturing relevant signals. You would compare offline and online distributions, then look at data pipelines and ensure real-time updates are not delayed. You could also ablate each component to pinpoint which part is underperforming.

10) Would you consider a multi-task approach for CTR and conversion?

The module can feed into a multi-task network that predicts both click and post-click conversion simultaneously. This way, shared representations might generalize better and you avoid training two completely separate modules. A multi-task loss must carefully balance the gradients so that one objective does not dominate.

No additional concluding remarks.

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