Generative AI for Recommendation Systems: A Guide to Tokenizing User Interaction Data

Large Language Models (LLMs) and other Generative AI models, particularly those based on the Transformer architecture [Vaswani et al., 2017], have shown incredible prowess in understanding and generating sequential data, primarily text. But what if we could apply this power to other kinds of sequences? User interaction data – the digital breadcrumbs of clicks, views, searches, and purchases we leave behind – is fundamentally sequential. Can we teach a generative model to understand and predict user behavior by representing these interactions as tokens?

The answer is a resounding yes. By carefully tokenizing heterogeneous user interaction data, we can unlock the capabilities of generative models for tasks like:

• Next-generation recommendation systems: Predicting the next item a user might interact with or purchase.
• Personalized user experiences: Generating tailored content or interface adjustments.
• Deep user behavior understanding: Discovering complex patterns and user journey archetypes.
• Synthetic data generation: Creating realistic user sessions for testing or training downstream models.

This post provides a practical guide on how to approach the tokenization of user interaction data for use with large generative AI models, often fine-tuning existing LLMs or training transformer-based models from scratch on this specific data type.

1. Defining and Collecting the Raw Material: User Interactions

Before tokenization, you need the right data. User interactions encompass any action a user takes within your digital environment. Common examples include:

• Content Interaction: Page views, article reads, video watches (including duration), music listens.
• E-commerce Actions: Product views, clicks, add-to-carts, wishlist additions, purchases, category browsing.
• Search Activity: Search queries entered.
• Engagement: Ratings, reviews, comments, likes, shares, button clicks.
• Implicit Signals: Hover times, scroll depth.

Effective data collection is crucial. Each event should be logged with key information:

• user_id: A unique identifier for the user (anonymized or pseudonymized for privacy).
• session_id: Groups actions within a single visit.
• timestamp: The precise time of the event (essential for ordering).
• event_type: A category defining the action (e.g., view, click, purchase).
• item_id: (If applicable) Identifier for the specific product, article, video, etc.
• context: Other relevant metadata like device type, location (handle with extreme care for privacy), search query text, item category, price, etc.

Ethical Considerations: Always prioritize user privacy. Anonymize data where possible, remove Personally Identifiable Information (PII), and comply with data protection regulations (like GDPR, CCPA). Be mindful of potential biases present in historical interaction data [Chen et al., 2023].

2. Preprocessing: From Raw Logs to Ordered Sequences

Raw logs need refinement:

• Cleaning: Filter out noise (e.g., bot traffic, erroneous events), handle missing values.
• Sessionization: Group events by user_id and session_id, then sort chronologically by timestamp. Define logical session boundaries (e.g., 30 minutes of inactivity). Long user histories might need to be split into manageable subsequences. Work on session-based recommendations highlights the importance of this step [Hidasi et al., 2016].
• Feature Engineering (Optional but helpful): Derive new features like time elapsed between events, day of the week, or categorize numerical features like price into bins.
• Structuring: Organize the data into chronologically ordered sequences of events per user or session.
  • Example: [Session Start -> View Item A -> Add Item A -> Search "query text" -> View Item B -> Purchase Item B -> Session End]

3. The Core Challenge: Unified Tokenization Strategy

This is where we bridge the gap between structured interaction data and the token-based input expected by Transformer models. Unlike natural language, user interaction sequences are heterogeneous, containing item IDs, event types, categories, text, and potentially numerical values.

The most common and flexible approach is to create a Unified Vocabulary with Special Tokens:

• Build a Vocabulary: Assign a unique integer ID (token) to every distinct element you want the model to understand.

  • Items (item_id): Each unique product, article, video, etc., gets its own token. item_123 -> 5001, item_456 -> 5002. Given potentially millions of items, you might limit this to items above a certain interaction frequency or use techniques like hashing if the vocabulary becomes too large.
  • Event Types (event_type): view -> 101, click -> 102, purchase -> 103.
  • Categorical Features: category_electronics -> 201, device_mobile -> 301.
  • Text (Search Queries, Reviews): Use a standard subword tokenizer (BPE, WordPiece, SentencePiece) – the amazing Hugging Face tokenizers library is perfect for this [HF Tokenizers Docs]. Train it on your text data (queries, reviews) and add the resulting subword tokens to your main vocabulary. search query "sci-fi novels" might become sci ##-fi novels tokens.
  • Numerical/Temporal Features: Discretize (bin) continuous values like price or time differences between events. Assign a token to each bin. time_delta_0-10s -> 401, price_$10-$50 -> 451.
  • Special Control Tokens: These are vital for structure and model processing:
    • <PAD>: For padding sequences to a fixed length.
    • <UNK>: For unknown items/features encountered post-training (or infrequent ones).
    • <SESSION_START>, <SESSION_END>: To delineate sessions.
    • <USER_START>, <USER_END>: (Optional) To delineate sequences belonging to a single user over multiple sessions.
    • <SEP>: A separator token, potentially used between different components of a single interaction event (e.g., between item and its category) or between the event type and the item.
    • Tokens representing feature types, e.g., <QUERY>, <CATEGORY>, <TIME_DELTA>.

• Tokenize Sequences: Convert each preprocessed interaction sequence into its corresponding sequence of integer token IDs based on the vocabulary you built.

Conceptual Tokenization Example:

• Interaction Event: (Timestamp: T1, Event: View, Item: 123, Category: Books)
• Possible Tokenized Representation: [<ITEM_ID_123>, <EVENT_VIEW>, <CATEGORY_BOOKS>]
  • (Or potentially incorporate time delta from previous event) [<TIME_DELTA_BIN_X>, <ITEM_ID_123>, <EVENT_VIEW>, <CATEGORY_BOOKS>]
• Full Session Sequence:
  • Raw: [View Item 123 (Books), 15s later, Search "sci-fi", 5s later, Click Item 456 (Books)]
  • Tokenized (Illustrative): [<SESSION_START>, <ITEM_ID_123>, <EVENT_VIEW>, <CAT_BOOKS>, <TIME_DELTA_10-20s>, <QUERY>, sci, ##-fi, <SEP>, <ITEM_ID_456>, <EVENT_CLICK>, <CAT_BOOKS>, <TIME_DELTA_0-10s>, <SESSION_END>]
  • Actual Input (IDs): [1, 5123, 101, 205, 402, 600, 15001, 15008, 10, 5456, 102, 205, 401, 2] (Example IDs)

Several works in sequential recommendation have implicitly used similar tokenization ideas when applying Transformer models [Kang & McAuley, 2018; Sun et al., 2019].

4. Formatting for Model Input

Transformer models usually expect fixed-size inputs:

• Padding & Truncation: Pad shorter sequences with the <PAD> token ID up to a maximum sequence length. Truncate longer sequences (usually keeping the most recent interactions).
• Attention Mask: Create a binary mask (sequence of 1s and 0s) of the same length as the input sequence. Use 1 for real tokens (including special tokens like <SESSION_START>) and 0 for <PAD> tokens. This tells the model's self-attention mechanism to ignore the padding.
• Input Dictionary: Structure the data for the model, typically using dictionaries expected by libraries like Hugging Face transformers:

# Example format
{
    "input_ids": [1, 5123, ..., 401, 2, 2, 2], # Padded token IDs
    "attention_mask": [1, 1, ..., 1, 0, 0, 0]  # 1 for real tokens, 0 for padding
    # "labels": [5123, 101, ..., 2, -100, -100, -100] # Often input_ids shifted for next-token prediction, ignore padding/inputs in loss
}

Resource: Check out Hugging Face Data Collators like DataCollatorForLanguageModeling for handling padding and label shifting [HF Data Collator Docs].

5. Training the Generative AI Model

With your data tokenized and formatted, you can train a model:

• Model Choice: Standard Transformer architectures (e.g., GPT-style decoder-only models, potentially T5-style encoder-decoders depending on the task) available in transformers are excellent choices. You can fine-tune a pre-trained LLM or train from scratch.
• Training Objective:
  • Next Token Prediction (Causal Language Modeling): This is the most natural fit. The model learns to predict the next token (interaction element) in the sequence given all preceding tokens. This directly models the sequential nature of user behavior. This is analogous to how models like GPT are trained on text [Radford et al., 2019].
  • Masked Modeling (like BERT/MLM): Mask out some tokens within the sequence and train the model to predict them based on bidirectional context. This can be useful for learning rich representations but is less directly generative for predicting the future.
  • Sequence-to-Sequence: Frame tasks like "predict purchase given session history" as input-output problems.
• Training: Use standard deep learning frameworks (PyTorch, TensorFlow) and the Hugging Face ecosystem (transformers, datasets, accelerate). Optimize using cross-entropy loss for prediction objectives.

6. Challenges and Considerations

• Scalability: User interaction logs can be enormous. Efficient data pipelines (e.g., using datasets) and distributed training are often necessary.
• Vocabulary Size: The number of unique items can be huge. Techniques like frequency-based cutoffs, item clustering, or hashing embeddings might be needed.
• Cold Start: How to handle new users or items not seen during training? Using <UNK> tokens, initializing new embeddings, or meta-learning approaches can help.
• Sequence Length: Very long user histories might exceed model capacity. Truncation, summarizing older history, or hierarchical modeling are options.
• Evaluation: Offline evaluation often uses recommendation metrics on held-out interactions (Recall@K, NDCG@K, MRR) [Jarvelin & Kekalainen, 2002]. Perplexity can measure generative quality. Online A/B testing is the gold standard for real-world impact.
• Temporal Dynamics: User interests drift. Incorporating time features (absolute time, time deltas) explicitly or using techniques sensitive to temporal shifts is important.

Conclusion

Tokenizing user interaction data transforms it into a format that powerful generative AI models can digest and learn from. By representing clicks, views, searches, and purchases as sequences of tokens, we move beyond simple collaborative filtering or content-based methods. This approach allows models to capture complex, sequential dependencies in user behavior, paving the way for more accurate predictions, deeper personalization, and a richer understanding of user journeys.

While challenges exist, particularly around scale and vocabulary management, the potential benefits of applying cutting-edge generative models to user interaction data are immense. The tools and techniques are rapidly evolving, making this an exciting frontier for anyone working with user data.

Get Started!

• Explore the Hugging Face datasets library for handling large datasets.
• Use the tokenizers library to build your custom unified vocabulary.
• Leverage the transformers library to train or fine-tune models like GPT-2, Llama, or T5 on your newly tokenized sequences.

We encourage you to experiment and share your findings with the community!

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References:

• [Chen et al., 2023] Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023). Bias and Debias in Recommender System: A Survey and Future Directions. ACM Transactions on Information Systems (TOIS).
• [Hidasi et al., 2016] Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2016). Session-based Recommendations with Recurrent Neural Networks. ICLR. (Pioneering work on sequence-aware recommendations using RNNs).
• [HF Data Collator Docs] Hugging Face Documentation. Data Collators. https://huggingface.co/docs/transformers/main_classes/data_collator
• [HF Tokenizers Docs] Hugging Face Documentation. Tokenizers Library. https://huggingface.co/docs/tokenizers/index
• [Jarvelin & Kekalainen, 2002] Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS). (Foundation for NDCG metric).
• [Kang & McAuley, 2018] Kang, W. C., & McAuley, J. (2018). Self-Attentive Sequential Recommendation. ICDM. (Introduced SASRec, using attention for sequential recommendation).
• [Radford et al., 2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Blog. (GPT-2 paper, demonstrating power of causal LM).
• [Sun et al., 2019] Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019). BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. CIKM. (Applied BERT-like masked modeling to recommendations).
• [Vaswani et al., 2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. NeurIPS. (Introduced the Transformer architecture).

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