InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features (2504.06609v1)

Abstract: Modern search systems use a multi-stage architecture to deliver personalized results efficiently. Key stages include retrieval, pre-ranking, full ranking, and blending, which refine billions of items to top selections. The pre-ranking stage, vital for scoring and filtering hundreds of thousands of items down to a few thousand, typically relies on two tower models due to their computational efficiency, despite often lacking in capturing complex interactions. While query-item cross interaction features are paramount for full ranking, integrating them into pre-ranking models presents efficiency-related challenges. In this paper, we introduce InteractRank, a novel two tower pre-ranking model with robust cross interaction features used at Pinterest. By incorporating historical user engagement-based query-item interactions in the scoring function along with the two tower dot product, InteractRank significantly boosts pre-ranking performance with minimal latency and computation costs. In real-world A/B experiments at Pinterest, InteractRank improves the online engagement metric by 6.5% over a BM25 baseline and by 3.7% over a vanilla two tower baseline. We also highlight other components of InteractRank, like real-time user-sequence modeling, and analyze their contributions through offline ablation studies. The code for InteractRank is available at https://github.com/pinterest/atg-research/tree/main/InteractRank.

InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features

The paper entitled "InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features" presents an advanced methodology tailored for optimizing the pre-ranking stage in large-scale search systems, particularly focusing on real-time personalization and efficiency. As part of the core architecture utilized by modern search systems, the pre-ranking stage is pivotal in narrowing down a large set of retrieved items to a manageable number that can be further processed in subsequent stages. This paper addresses the inefficiencies of traditional methods and proposes a novel solution through InteractRank, which integrates both sophisticated feature engineering and predictive modeling.

Central to the methodology is the refinement of the two-tower model architecture, which remains prevalent due to its computational efficiency despite its inherent limitations in capturing complex query-item interactions. The authors augment this model by introducing robust cross interaction features that can incorporate historical user engagement data into the ranking process, thus enhancing the model's ability to discern nuanced user preferences and item relevancy in real-time.

Key Contributions and Methodology

The InteractRank model performs several notable innovations:

1. Enhanced Two Tower Architecture: The paper introduces cross interaction features that are seamlessly integrated into the two tower model. These features are designed to capture complex query-item interactions by utilizing historical engagement data, thereby overcoming the classical two tower model's limitations.
2. Implementation of Real-time User Engagement Sequence Modeling: Incorporating real-time engagement sequences allows the model to maintain a dynamic understanding of user preferences and contextual information, enabling a responsive personalization at web-scale.
3. Development of ItemQueryPerf Features: The authors propose a framework known as ItemQueryPerf (IQP) for generating query-item cross-interaction features. These are derived from extensive user engagement data over different time windows, ensuring that both short-term and long-term trends are accounted for. This framework enhances the model's capacity to predict user engagement more accurately.
4. Efficient System Design for Real-Time Deployment: From an architectural perspective, the solution is engineered for deployment within high-demand environments like Pinterest, ensuring minimal latency and maximum scalability. The proposed system integrates smoothly with existing infrastructure by maintaining efficient offline batch processing and leveraging fast in-memory lookups for online serving.

Results and Evaluations

The empirical evaluations through offline and online A/B testing highlight the effectiveness of InteractRank in improving user engagement metrics. The model is shown to enhance the online engagement by 6.5% over a baseline BM25 model and by 3.7% over a vanilla two tower baseline. These results are particularly significant given the stringent latency constraints typical of high-volume recommendation systems.

Through comprehensive ablation studies, the paper further isolates and examines the contributions of individual components of the InteractRank framework. These analyses reinforce the utility of incorporating cross interaction features and user sequence modeling to achieve superior performance in both offline predictions and real-world user engagement.

Implications and Future Directions

The implications of this research are profound for both the theoretical underpinnings of recommendation system design and their practical applications in commercial search engines. The introduction of cross interaction features and their deployment at scale reveals new pathways for leveraging historical user interactions without incurring prohibitive computational costs.

Looking forward, the research may catalyze further exploration into blending such feature-based methodologies with other advanced machine learning techniques like reinforcement learning, where the system itself could be trained iteratively based on real-time feedback to refine the personalization process continuously. Additionally, as AI evolves, there is potential to examine how these models could be generalized or adapted for other domains requiring scalable personalization and complex decision making under time constraints.

In conclusion, the InteractRank framework exemplifies a significant advancement in the personalization of search pre-ranking, providing a robust model that aligns efficiency with enhanced contextual understanding and interaction modeling. This positions it as a substantial contribution to the ongoing development of high-performance recommendation systems in today’s data-intensive environments.