Modern recommender systems leverage large-scale retrieval models consisting
of two stages: training a dual-encoder model to embed queries and candidates in
the same space, followed by an Approximate Nearest Neighbor (ANN) search to
select top candidates given a query's embedding. In this paper, we propose a
new single-stage paradigm: a generative retrieval model which autoregressively
decodes the identifiers for the target candidates in one phase. To do this,
instead of assigning randomly generated atomic IDs to each item, we generate
Semantic IDs: a semantically meaningful tuple of codewords for each item that
serves as its unique identifier. We use a hierarchical method called RQ-VAE to
generate these codewords. Once we have the Semantic IDs for all the items, a
Transformer based sequence-to-sequence model is trained to predict the Semantic
ID of the next item. Since this model predicts the tuple of codewords
identifying the next item directly in an autoregressive manner, it can be
considered a generative retrieval model. We show that our recommender system
trained in this new paradigm improves the results achieved by current SOTA
models on the Amazon dataset. Moreover, we demonstrate that the
sequence-to-sequence model coupled with hierarchical Semantic IDs offers better
generalization and hence improves retrieval of cold-start items for
recommendations.

提出一种新颖的单阶段生成检索模型，将 Amazon 数据集用于训练，结果表明，这种模型能够获得更好的推荐效果和更好的泛化能力以推荐 “冷启动” 物品。

基于生成检索的推荐系统

Recommender Systems with Generative Retrieval

Knowledge-intensive language tasks (KILT) usually require a large body of
information to provide correct answers. A popular paradigm to solve this
problem is to combine a search system with a machine reader, where the former
retrieves supporting evidences and the latter examines them to produce answers.
Recently, the reader component has witnessed significant advances with the help
of large-scale pre-trained generative models. Meanwhile most existing solutions
in the search component rely on the traditional ``index-retrieve-then-rank''
pipeline, which suffers from large memory footprint and difficulty in
end-to-end optimization. Inspired by recent efforts in constructing model-based
IR models, we propose to replace the traditional multi-step search pipeline
with a novel single-step generative model, which can dramatically simplify the
search process and be optimized in an end-to-end manner. We show that a strong
generative retrieval model can be learned with a set of adequately designed
pre-training tasks, and be adopted to improve a variety of downstream KILT
tasks with further fine-tuning. We name the pre-trained generative retrieval
model as CorpusBrain as all information about the corpus is encoded in its
parameters without the need of constructing additional index. Empirical results
show that CorpusBrain can significantly outperform strong baselines for the
retrieval task on the KILT benchmark and establish new state-of-the-art
downstream performances. We also show that CorpusBrain works well under zero-
and low-resource settings.

本文提出了一种基于生成式检索模型的信息检索方法，取代了传统的多步骤索引 - 检索 - 排序流程。该方法能带来显著的简化和端到端的优化，实现了对知识密集语言任务的有效检索和提高，相关检索系统名为 CorpusBrain。