TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios

TL;DR

TBGRecall introduces a novel generative retrieval framework with Next Session Prediction for improved e-commerce recommendations, demonstrating superior performance and scalability over existing methods through extensive experiments.

Contribution

The paper presents TBGRecall, a new generative retrieval model incorporating NSP and optimized training strategies, advancing recommendation effectiveness in e-commerce.

Findings

Outperforms state-of-the-art recommendation methods.

Shows a clear scaling law trend.

Effective on large-scale industrial datasets.

Abstract

Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative models have demonstrated potential in enhancing recommendation systems; however, these models often exhibit limitations in optimizing retrieval tasks, primarily due to their reliance on autoregressive generation mechanisms. Conventional approaches introduce sequential dependencies that impede efficient retrieval, as they are inherently unsuitable for generating multiple items without positional constraints within a single request session. To address these limitations, we propose TBGRecall, a framework integrating Next Session Prediction (NSP), designed to enhance generative retrieval models for e-commerce applications. Our framework reformulation involves partitioning input samples into multi-session sequences, where each sequence comprises a session token followed by a set of item tokens, and then further incorporate multiple optimizations tailored to the generative task in retrieval scenarios. In terms of training methodology, our pipeline integrates limited historical data pre-training with stochastic partial incremental training, significantly improving training efficiency and emphasizing the superiority of data recency over sheer data volume. Our extensive experiments, conducted on public benchmarks alongside a large-scale industrial dataset from TaoBao, show TBGRecall outperforms the state-of-the-art recommendation methods, and exhibits a clear scaling law trend. Ultimately, NSP represents a significant advancement in the effectiveness of generative recommendation systems for e-commerce applications.