The paper presents Hiformer, an innovative Transformer-based model tailored for recommender systems, emphasizing efficient heterogeneous feature interaction learning. Traditional Transformer architectures face significant hurdles in recommender systems, notably in capturing the complex interplay of diverse features and achieving acceptable serving latency for web-scale applications.
Hiformer introduces a heterogeneous attention layer to better capture the nuanced interactions among varied features, which traditional models often overlook. This approach is not only more expressive but also maintains the scalability essential for large-scale deployments.
To mitigate the high latency typically associated with Transformer models, Hiformer employs low-rank approximation and model pruning strategies. These techniques significantly reduce computational demands without sacrificing model performance, making Hiformer a viable option for real-time recommender systems.
The deployment of Hiformer in Google Play's App ranking model serves as a compelling case study of its effectiveness. The model not only outperformed state-of-the-art (SOTA) recommendation models in offline experiments but also demonstrated considerable gains in key engagement metrics during online A/B testing.
The Hiformer model introduces a novel approach to handling the dynamic semantics inherent in feature interactions within recommender systems through its heterogeneous attention layer. This layer is specially designed to adapt to the context-dependent nature of feature interactions, enabling the model to capture the complex relationships between features more effectively than traditional approaches.
The heterogeneous attention layer is pivotal to Hiformer's ability to understand and model the intricate collaborative effects among diverse features. It diverges from the vanilla attention mechanism by employing distinct parameterizations for different feature types, allowing for a more granular and context-aware processing of feature interactions.
Hiformer's heterogeneous attention layer, with its ability to accommodate different parameterizations for various features, is adept at capturing such dynamic semantics. This marks a significant improvement over models that process all features uniformly, potentially missing out on the nuanced relationships that define effective recommender systems.
While vanilla attention mechanisms in Transformers are adept at capturing syntactic and semantic relationships within sequences, their application in recommender systems presents unique challenges. Vanilla Transformers project all features through the same projection matrices (Wq and Wk), treating all features uniformly without accounting for the dynamic, context-dependent semantics prevalent in recommender systems.
A key discussion point arises when considering alternative methods like Masked Language Models (MLMs) for learning feature interactions. MLMs, exemplified by BERT, have revolutionized fields like NLP by learning deep contextual relationships between words. This prompts the question: Could MLMs be adapted to learn embeddings for feature interactions in recommender systems?
Both Hiformer and MLMs excel in capturing context, yet they approach it differently. Hiformer's heterogeneous attention mechanism is specifically designed to account for the dynamic nature of feature semantics in recommender systems. It adapts to the context where the meaning of features can change, a challenge that traditional MLMs, designed for static textual contexts, might not address as effectively.
MLMs are known for their substantial computational requirements, which could lead to high latency, making them less suitable for real-time applications like web-scale recommender systems. Hiformer, on the other hand, incorporates low-rank approximation and model pruning to mitigate these issues, aiming for a balance between expressiveness and efficiency.
The training objectives of MLMs and Hiformer also differ significantly. While MLMs are trained to predict masked tokens within large text corpora, Hiformer is tailored for the specific task of feature interaction learning in recommender systems, potentially making it more directly applicable to this domain.
While MLMs offer a powerful framework for understanding contextual relationships, Hiformer's specialized design for recommender systems provides a more focused solution for learning feature interactions. It addresses the unique challenges of this domain, such as dynamic feature semantics and the necessity for low-latency predictions, making it a compelling choice for web-scale recommender systems.
In conclusion, Hiformer stands out as a groundbreaking advancement in recommender systems, offering a potent blend of expressiveness, efficiency, and real-world applicability.
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Created 2024-02-14T06:13:48-08:00, updated 2024-02-22T04:55:37-08:00 · History · Edit