Hi-Gen: Generative Retrieval For Large-Scale Personalized E-commerce Search

Enhancing Large-Scale Personalized E-commerce Search with Hierarchical Generative Retrieval

Introduction

Recent advancements in Generative Retrieval (GR) techniques have shown promising directions for enhancing search systems, but applying these methods to large-scale E-commerce search systems introduces unique challenges in encoding and decoding document identifiers (docIDs). The paper presents a novel method, Hierarchical encoding-decoding Generative retrieval (Hi-Gen), which addresses several shortcomings of existing generative retrieval models in the context of large-scale personalized E-commerce searches.

Key Methodological Innovations

Representation Learning and Metric Learning: Hi-Gen introduces a dual-component model to capture both semantic relevance and efficiency information of items:

• Representation Learning: This component is responsible for generating discriminative embeddings that integrate semantic, common, and efficiency-based features of items using a multi-task learning framework.
• Metric Learning: Post the initial embeddings, a metric learning module refines these embeddings further to ensure that similar items cluster closely in the embedded space, by employing a triplet margin loss that prioritizes both user interactions and item efficiency.

Category-Guided Hierarchical Clustering for docID Generation: Rather than relying on traditional methods, Hi-Gen uses a unique category-guided hierarchical clustering to generate docIDs. This method leverages the hierarchical category structure inherent to E-commerce platforms to generate meaningful and structured identifiers encompassing both item category and efficiency metrics.

Decoding with Position-Aware Loss: A position-aware loss function is employed to fine-tune the language model that decodes user queries into docIDs. This loss function emphasizes the positional significance of tokens in the docIDs, aiming to capture the hierarchical importance and inter-token relationships more accurately.

Experimental Validation

Quantitative Improvements: Hi-Gen demonstrates significant improvements over state-of-the-art models in well-known metrics. Specifically, the paper reports a 3.30% and 4.62% increase over existing best models in Recall@1 in public and industry-specific datasets, respectively. Larger gains are noted when exploring metrics like Recall@10, showcasing Hi-Gen's robustness in retrieving relevant documents.

Ablation Study: Removing any single component from Hi-Gen (such as position-aware loss or metric learning) leads to notable performance dips, underlining the significance of each component in the integrated model. The category-guided clustering emerges as a particularly critical feature, its removal resulting in the most substantial performance decrease.

Zero-Shot and Scalability Tests: Hi-Gen's generalization capabilities are compelling, particularly in zero-shot learning scenarios where it significantly outperforms other models. The adaptability of Hi-Gen is further validated through scalable online A/B testing on a large-scale E-commerce platform, demonstrating improvements in key business metrics such as Recall, GMV, and conversion rates without incurring additional latency overheads.

Implications and Future Work

The innovation of Hi-Gen lies in its holistic approach, covering feature representation, structured docID generation, and fine-tuning retrieval through position-aware mechanisms. This methodology could set a new standard for personalized search in massive, dynamic inventories typical of modern E-commerce platforms. Future explorations could refine the encoding strategies or adapt the model for other types of personalized recommendation systems, potentially exploring the integration with other forms of machine learning models to enhance decision-making processes further. Additionally, further elaboration on the impacts of different features and embedding layers could provide deeper insights into the contributions of individual model components.