Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations

Anima Singh; Trung Vu; Nikhil Mehta; Raghunandan Keshavan; Maheswaran Sathiamoorthy; Yilin Zheng; Lichan Hong; Lukasz Heldt; Li Wei; Devansh Tandon; Ed H. Chi; Xinyang Yi

arXiv · 2306.08121

Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations

Anima Singh·Trung Vu·Nikhil Mehta·Raghunandan Keshavan·Maheswaran Sathiamoorthy·Yilin Zheng·Lichan Hong·Lukasz Heldt·Li Wei·Devansh Tandon·Ed H. Chi·Xinyang Yi

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Abstract

Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, causing problems of learning unseen and long-tail items, especially when item corpus is large, power-law distributed, and evolving dynamically. In this paper, we propose using content-derived features as a replacement for random ids. We show that simply replacing ID features with content-based embeddings can cause a drop in quality due to reduced memorization capability. To strike a good balance of memorization and generalization, we propose to use Semantic IDs -- a compact discrete item representation learned from frozen content embeddings using RQ-VAE that captures the hierarchy of concepts in items -- as a replacement for random item ids. Similar to content embeddings, the compactness of Semantic IDs poses a problem of easy adaption in recommendation models. We propose novel methods for adapting Semantic IDs in industry-scale ranking models, through hashing sub-pieces of of the Semantic-ID sequences. In particular, we find that the SentencePiece model that is commonly used in LLM tokenization outperforms manually crafted pieces such as N-grams. To the end, we evaluate our approaches in a real-world ranking model for YouTube recommendations. Our experiments demonstrate that Semantic IDs can replace the direct use of video IDs by improving the generalization ability on new and long-tail item slices without sacrificing overall model quality.

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Anima Singh, Trung Vu, Nikhil Mehta, Raghunandan Keshavan, Maheswaran Sathiamoorthy, Yilin Zheng, Lichan Hong, Lukasz Heldt, Li Wei, Devansh Tandon, Ed H. Chi, Xinyang Yi. 2023-06-13. Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations. https://arxiv.org/abs/2306.08121

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