Generative retrieval methods utilize generative sequential modeling techniques, such as transformers, to generate candidate items for recommender systems. These methods have demonstrated promising results in academic benchmarks, surpassing traditional retrieval models like two-tower architectures. However, current generative retrieval methods lack the scalability required for industrial recommender systems, and they are insufficiently flexible to satisfy the multiple metric requirements of modern systems. This paper introduces PinRec, a novel generative retrieval model developed for applications at Pinterest. PinRec utilizes outcome-conditioned generation, enabling modelers to specify how to balance various outcome metrics, such as the number of saves and clicks, to effectively align with business goals and user exploration. Additionally, PinRec incorporates multi-token generation to enhance output diversity while optimizing generation. Our experiments demonstrate that PinRec can successfully balance performance, diversity, and efficiency, delivering a significant positive impact to users using generative models. This paper marks a significant milestone in generative retrieval, as it presents, to our knowledge, the first rigorous study on implementing generative retrieval at the scale of Pinterest.

翻译：生成式检索方法利用生成式序列建模技术（如Transformer）为推荐系统生成候选项目。这些方法在学术基准测试中展现出优于传统检索模型（如双塔架构）的潜力。然而，当前的生成式检索方法缺乏工业级推荐系统所需的可扩展性，且灵活性不足，难以满足现代系统的多指标需求。本文提出PinRec，一种专为Pinterest应用设计的新型生成式检索模型。PinRec采用结果条件化生成技术，使建模者能够指定如何平衡多种结果指标（例如保存次数与点击次数），以有效对齐业务目标与用户探索需求。此外，PinRec结合多标记生成机制，在优化生成过程的同时增强输出多样性。实验表明，PinRec能成功平衡性能、多样性与效率，通过生成式模型为用户带来显著正向影响。据我们所知，本文首次系统性地研究了在Pinterest规模下实施生成式检索的方案，标志着该领域的重要里程碑。