Efficient Generative Retrieval for Large-Scale Personalized E-commerce Search

Core Concepts

Hi-Gen, an efficient generative retrieval method, enhances large-scale personalized E-commerce search systems by encoding both semantic relevance and efficiency information in document identifiers (docIDs) and leveraging position-aware loss to improve decoding performance.

Abstract

The paper proposes Hi-Gen, an efficient generative retrieval method for large-scale personalized E-commerce search systems.
Key highlights:

Hi-Gen introduces a novel algorithm for docID generation that learns discriminative feature representations of items to capture both semantic relevance and efficiency information, and leverages category-guided hierarchical clustering to generate semantically structured docIDs.
A position-aware loss is proposed to enhance the performance of the language model used in the decoding stage, aiming to discriminate the importance of positions and mine the semantic and efficiency differences among various tokens at the same position.
Two variants of Hi-Gen (Hi-Gen-I2I and Hi-Gen-Cluster) are designed to support online real-time large-scale recall in the online serving process.
Extensive experiments on both public and industry datasets demonstrate the effectiveness and efficiency of Hi-Gen, outperforming state-of-the-art methods. Deploying Hi-Gen to a large-scale E-commerce platform leads to significant improvements in online metrics.
Hi-Gen beats the basic Differentiable Search Index (DSI) model and BM25 in zero-shot learning scenarios, proving its generalization capabilities.

Stats

The paper reports the following key metrics:

Recall@1 and Recall@10 on the AOL4PS dataset
Recall@1, Recall@50, and Recall@100 on the AEDST dataset
RecallNum, CTR, CVR, PayCount, and GMV in the online A/B experiment

Quotes

"Leveraging generative retrieval (GR) techniques to enhance search systems is an emerging methodology that has shown promising results in recent years."
"To overcome these problems, we introduce an efficient Hierarchical encoding-decoding Generative retrieval method (Hi-Gen) for large-scale personalized E-commerce search systems."
"Extensive experiments on both public and industry datasets demonstrate the effectiveness and efficiency of Hi-Gen. It gets 3.30% and 4.62% improvements over SOTA for Recall@1 on the public and industry datasets, respectively."

Key Insights Distilled From

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

by Yanjing Wu,Y... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15675.pdf

Deeper Inquiries

How can the proposed position-aware loss mechanism be extended to other generative retrieval models beyond Hi-Gen

The position-aware loss mechanism proposed in Hi-Gen can be extended to other generative retrieval models by incorporating it into the training process of these models. The key idea behind the position-aware loss is to assign different weights to the positional information of tokens in the docID based on their significance. This helps the model to learn the importance of different positions and improve the overall performance of the language model during decoding.
To extend this mechanism to other models, researchers can integrate a similar loss function into the training pipeline of existing generative retrieval models. By incorporating position-awareness into the training process, these models can learn to better capture the dependencies and relationships between different tokens in the generated docIDs. This can lead to more accurate and contextually relevant results in the retrieval process.

What are the potential limitations of the category-guided hierarchical clustering approach, and how can it be further improved to handle more complex product hierarchies

The category-guided hierarchical clustering approach, while effective in capturing semantic information and organizing products based on their categories, may have limitations when dealing with more complex product hierarchies. Some potential limitations of this approach include:

Scalability: As the product hierarchy becomes more intricate and extensive, the clustering process may become computationally intensive and challenging to manage.

Category Ambiguity: In cases where products belong to multiple categories or subcategories, the hierarchical clustering approach may struggle to accurately assign docIDs based on the semantic relevance of the products.

To address these limitations and improve the approach for handling complex product hierarchies, several strategies can be considered:

Enhanced Hierarchical Clustering Algorithms: Developing more advanced clustering algorithms that can efficiently handle large-scale and complex hierarchies while maintaining semantic coherence.

Multi-Level Clustering: Implementing a multi-level clustering approach where products are clustered at different levels of the hierarchy to capture more nuanced relationships and improve the accuracy of docID assignment.

Dynamic Category Assignment: Introducing a dynamic category assignment mechanism that adapts to changes in product categorization and hierarchy, ensuring flexibility and accuracy in docID generation.

By addressing these limitations and incorporating advanced techniques, the category-guided hierarchical clustering approach can be further improved to handle more complex product hierarchies effectively.

Given the success of Hi-Gen in E-commerce search, how can the ideas be applied to other domains, such as general web search or recommendation systems, to enhance their performance

The success of Hi-Gen in E-commerce search can be applied to other domains, such as general web search or recommendation systems, to enhance their performance in the following ways:

Web Search: In general web search, Hi-Gen's approach of incorporating efficient and semantic information into docID generation can improve the relevance and accuracy of search results. By learning discriminative feature representations and utilizing position-aware loss, web search engines can provide more personalized and contextually relevant search results to users.

Recommendation Systems: In recommendation systems, Hi-Gen's category-guided hierarchical clustering approach can be leveraged to organize and recommend products or content based on their semantic relationships. By enhancing the clustering process and incorporating efficient information, recommendation systems can offer more personalized and targeted recommendations to users.

Cross-Domain Applications: The ideas and techniques used in Hi-Gen can be adapted and applied to various domains beyond E-commerce, such as healthcare, finance, or entertainment. By customizing the model architecture and training process to suit the specific requirements of different domains, the performance and effectiveness of search and recommendation systems can be enhanced across diverse industries.