# Unified Semantic and ID Representation Learning for Deep Recommenders

arXiv: https://arxiv.org/abs/2502.16474 · HTML: https://mlsanity.com/paper/2502.16474/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2025.

## Built on by (most cited first, 8 of 8)

- [GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation](https://mlsanity.com/paper/2604.14878/) (2026)
- [LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation](https://mlsanity.com/paper/2511.14221/) (2025)
- [PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation](https://mlsanity.com/paper/2508.18166/) (2025)
- [Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation](https://mlsanity.com/paper/2511.20673/) (2025)
- [The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation](https://mlsanity.com/paper/2512.10388/) (2025)
- [Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation](https://mlsanity.com/paper/2607.27944/) (2026)
- [Exploring Bottom-Up Clustering for Creating Semantic IDs](https://mlsanity.com/paper/2609.08310/) (2026)
- [FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation](https://mlsanity.com/paper/2609.36670/) (2026)

## Abstract (arXiv)

Effective recommendation is crucial for large-scale online platforms. Traditional recommendation systems primarily rely on ID tokens to uniquely identify items, which can effectively capture specific item relationships but suffer from issues such as redundancy and poor performance in cold-start scenarios. Recent approaches have explored using semantic tokens as an alternative, yet they face challenges, including item duplication and inconsistent performance gains, leaving the potential advantages of semantic tokens inadequately examined. To address these limitations, we propose a Unified Semantic and ID Representation Learning framework that leverages the complementary strengths of both token types. In our framework, ID tokens capture unique item attributes, while semantic tokens represent shared, transferable characteristics. Additionally, we analyze the role of cosine similarity and Euclidean distance in embedding search, revealing that cosine similarity is more effective in decoupling accumulated embeddings, while Euclidean distance excels in distinguishing unique items. Our framework integrates cosine similarity in earlier layers and Euclidean distance in the final layer to optimize representation learning. Experiments on three benchmark datasets show that our method significantly outperforms state-of-the-art baselines, with improvements ranging from 6% to 17% and a reduction in token size by over 80%. These results demonstrate the effectiveness of combining ID and semantic tokenization to enhance the generalization ability of recommender systems.