# The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation

arXiv: https://arxiv.org/abs/2512.10388 · HTML: https://mlsanity.com/paper/2512.10388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Autoregressive Image Generation using Residual Quantization](https://mlsanity.com/paper/2203.01941/) (2022)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://mlsanity.com/paper/2210.12316/) (2022)
- [Generative Recommendation: Towards Next-generation Recommender Paradigm](https://mlsanity.com/paper/2304.03516/) (2023)
- [LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation](https://mlsanity.com/paper/2405.20646/) (2024)
- [Long-tail Session-based Recommendation](https://mlsanity.com/paper/2007.12329/) (2020)
- [Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID](https://mlsanity.com/paper/2504.02137/) (2025)
- [LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation](https://mlsanity.com/paper/2409.19925/) (2024)
- [SIGMA: Selective Gated Mamba for Sequential Recommendation](https://mlsanity.com/paper/2408.11451/) (2024)
- [CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation](https://mlsanity.com/paper/2105.10868/) (2021)
- [Unified Semantic and ID Representation Learning for Deep Recommenders](https://mlsanity.com/paper/2502.16474/) (2025)
- [Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation](https://mlsanity.com/paper/2503.12183/) (2025)
- [Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation](https://mlsanity.com/paper/2508.20359/) (2025)
- [Taming the Long Tail: Denoising Collaborative Information for Robust Semantic ID Generation](https://mlsanity.com/paper/2510.25622/) (2025)
- [PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation](https://mlsanity.com/paper/2508.18166/) (2025)

## Abstract (arXiv)

Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical user-item interactions. However, such embeddings are vulnerable in long-tail scenarios where most items are rarely consumed. Recent methods that incorporate auxiliary information often face noisy collaborative sharing from co-occurrence signals or semantic homogeneity caused by flat dense embeddings. In contrast, Semantic IDs (SID), with their support for code sharing and multi-granular semantic modeling, offer a promising alternative. Nevertheless, SID-based methods are hindered by a collaborative overwhelming phenomenon: commonly adopted quantization mechanisms compromise the identifier uniqueness needed to model head items, resulting in a performance trade-off between head and tail items. To address this challenge, we propose H2Rec, a novel framework that harmonizes SID and HID. We design a dual-branch modeling architecture that simultaneously captures the multi-granular semantics of SID while preserving the unique collaborative identity provided by HID. Moreover, we introduce a dual-level alignment strategy to bridge the two representations, enabling effective knowledge transfer and robust preference modeling. Extensive offline experiments on three public benchmarks and online experiments on a large-scale commercial platform demonstrate that H2Rec achieves a better balance between head and tail recommendation quality and consistently outperforms existing baselines.