Trillion-Parameter Sequential Transducers for Scalable Generative Recommendations

Core Concepts

Generative Recommenders (GRs) reformulate recommendation tasks as sequential transduction problems, enabling the training and deployment of trillion-parameter models that significantly outperform traditional Deep Learning Recommendation Models (DLRMs) in large-scale industrial settings.

Abstract

The authors propose a new paradigm called Generative Recommenders (GRs) that reformulates recommendation tasks as sequential transduction problems. This allows them to unify the heterogeneous feature space used in traditional DLRMs and cast ranking and retrieval as pure sequential transduction tasks. Key highlights: GRs sequentialize and unify the heterogeneous feature space in DLRMs, enabling the use of powerful sequential transduction architectures. The authors propose a new encoder design called Hierarchical Sequential Transduction Unit (HSTU) that is optimized for large, non-stationary vocabularies and leverages sparsity in recommendation data. HSTU-based GRs, with up to 1.5 trillion parameters, outperform DLRMs by up to 65.8% in NDCG and are 5.3x to 15.2x faster than Transformer-based models. The authors show that the model quality of GRs scales as a power-law of training compute, similar to large language models, reducing the carbon footprint needed for future model developments. GR models deployed on a large internet platform with billions of users improve online metrics by 12.4%, demonstrating the practical benefits of the proposed approach.

Stats

GR models with 1.5 trillion parameters improve online metrics by 12.4% compared to traditional DLRMs. HSTU-based GRs outperform Transformer-based models by up to 65.8% in NDCG and are 5.3x to 15.2x faster on 8192 length sequences.

Quotes

"Generative Recommenders (GRs), with 1.5 trillion parameters, improve metrics in online A/B tests by 12.4% and have been deployed on multiple surfaces of a large internet platform with billions of users." "More importantly, the model quality of Generative Recommenders empirically scales as a power-law of training compute across three orders of magnitude, up to GPT-3/LLaMa-2 scale, which reduces carbon footprint needed for future model developments, and further paves the way for the first foundational models in recommendations."

Key Insights Distilled From

Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

by Jiaqi Zhai,L... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2402.17152.pdf

Deeper Inquiries

How can the proposed GR framework be extended to handle multi-modal data (e.g., text, images, videos) in recommendation systems

The proposed Generative Recommender (GR) framework can be extended to handle multi-modal data in recommendation systems by incorporating different types of features such as text, images, and videos. This extension would involve modifying the input data representation to include multiple modalities and designing the architecture to process and extract relevant information from each modality. Here are some key steps to extend the GR framework for multi-modal data: Feature Fusion: Combine the different modalities of data (text, images, videos) into a unified feature space. This can be done by using techniques like late fusion, early fusion, or cross-modal attention mechanisms to capture the relationships between different modalities. Multi-Modal Embeddings: Create embeddings for each modality and learn joint embeddings that capture the interactions between different modalities. This can help in capturing the complementary information from each modality. Multi-Modal Attention: Design attention mechanisms that can attend to different modalities based on the context of the recommendation task. This can help the model focus on relevant modalities for making recommendations. Multi-Task Learning: Incorporate multi-task learning to leverage the strengths of each modality for different aspects of the recommendation task. For example, text data might be more informative for content-based recommendations, while images and videos could be more useful for visual recommendations. Evaluation and Fine-Tuning: Evaluate the performance of the multi-modal GR model using appropriate metrics for recommendation systems. Fine-tune the model based on the evaluation results to optimize performance. By extending the GR framework to handle multi-modal data, recommendation systems can leverage the richness of information available in different modalities to provide more personalized and accurate recommendations to users.

What are the potential challenges and limitations of applying the scaling law observed in GRs to other domains beyond recommendations

The scaling law observed in Generative Recommenders (GRs) for recommendation systems may face challenges and limitations when applied to other domains beyond recommendations. Some potential challenges and limitations include: Data Complexity: Other domains may have different data distributions and complexities compared to recommendation systems. The scaling law observed in GRs for recommendations may not directly translate to these domains, requiring domain-specific adaptations. Model Generalization: The scaling law in GRs for recommendations may be specific to the characteristics of recommendation tasks, such as user interactions and item preferences. Generalizing this scaling law to other tasks like language modeling or machine translation may require additional considerations for the nature of the data and the task requirements. Computational Resources: The scalability of models based on the observed scaling law may be limited by the availability of computational resources in other domains. Tasks like language modeling or machine translation may have different computational requirements that could impact the applicability of the scaling law. Task-Specific Architectures: Different tasks may require task-specific architectures and design choices that are not directly transferable from recommendation systems. Adapting the insights from GRs to other domains would involve rethinking the model architecture and training strategies to suit the specific task requirements. Evaluation Metrics: The performance metrics and evaluation criteria in other domains may differ from those in recommendation systems. Adapting the scaling law to new domains would require defining appropriate evaluation metrics and benchmarks to assess the model's performance effectively. While the scaling law observed in GRs for recommendations provides valuable insights into model scalability, its application to other domains would require careful consideration of the unique characteristics and challenges of those domains.

How can the insights from GRs be leveraged to develop more efficient and scalable architectures for other types of sequential transduction tasks, such as language modeling or machine translation

The insights from Generative Recommenders (GRs) can be leveraged to develop more efficient and scalable architectures for other types of sequential transduction tasks, such as language modeling or machine translation, by incorporating the following strategies: Unified Feature Space: Like in GRs, create a unified feature space that captures the sequential nature of the data and enables efficient processing of heterogeneous features. This can help in handling diverse input modalities and improving model performance. Efficient Attention Mechanisms: Design attention mechanisms that optimize computational resources and reduce memory usage. Techniques like pointwise attention, sparse attention, and efficient attention kernels can enhance the scalability of the model. Sparsity Techniques: Implement sparsity techniques like Stochastic Length to increase efficiency and reduce computational costs. By leveraging sparsity in the input sequences, models can process long sequences more effectively. Cost-Amortization: Develop algorithms like M-FALCON that enable cost-amortization during inference, allowing for the processing of a large number of candidates with a constant inference budget. This can improve throughput and scalability for sequential transduction tasks. Scalability Analysis: Conduct scalability analysis to understand how model performance scales with computational resources. By studying the scaling behavior of the model, it is possible to optimize hyperparameters and architecture for efficient training and inference. By applying these insights from GRs, architectures for language modeling, machine translation, and other sequential transduction tasks can be enhanced to achieve better efficiency, scalability, and performance.