Training good representations for items is critical in recommender models.
Typically, an item is assigned a unique randomly generated ID, and is commonly
represented by learning an embedding corresponding to the value of the random
ID. Although widely used, this approach have limitations when the number of
items are large and items are power-law distributed -- typical characteristics
of real-world recommendation systems. This leads to the item cold-start
problem, where the model is unable to make reliable inferences for tail and
previously unseen items. Removing these ID features and their learned
embeddings altogether to combat cold-start issue severely degrades the
recommendation quality. Content-based item embeddings are more reliable, but
they are expensive to store and use, particularly for users' past item
interaction sequence. In this paper, we use Semantic IDs, a compact discrete
item representations learned from content embeddings using RQ-VAE that captures
hierarchy of concepts in items. We showcase how we use them as a replacement of
item IDs in a resource-constrained ranking model used in an industrial-scale
video sharing platform. Moreover, we show how Semantic IDs improves the
generalization ability of our system, without sacrificing top-level metrics.

该论文通过使用基于 RQ-VAE 的语义 ID 替代随机生成 ID 的方式来解决推荐系统中的冷启动问题，并展示了语义 ID 对模型的泛化能力的提升。

语义 ID 提高泛化能力： 推荐排名案例研究

Better Generalization with Semantic IDs: A case study in Ranking for  Recommendations

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。