SIDE: Semantic ID Embedding for effective learning from sequences

• URL: http://arxiv.org/abs/2506.16698v1
• Date: Fri, 20 Jun 2025 02:40:38 GMT
• Title: SIDE: Semantic ID Embedding for effective learning from sequences
• Authors: Dinesh Ramasamy, Shakti Kumar, Chris Cadonic, Jiaxin Yang, Sohini Roychowdhury, Esam Abdel Rhman, Srihari Reddy,
• Abstract summary: Sequence-based recommendations systems are driving the state-of-the-art for industrial ad-recommendation systems.<n>We propose a novel approach that leverages vector quantization (VQ) to inject a compact Semantic ID (SID) as input to the recommendation models.<n>The proposed enhancements when applied to a large-scale industrial ads-recommendation system achieves 2.4X improvement in normalized entropy (NE) gain and 3X reduction in data footprint.
• Score: 1.2145532233226686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sequence-based recommendations models are driving the state-of-the-art for industrial ad-recommendation systems. Such systems typically deal with user histories or sequence lengths ranging in the order of O(10^3) to O(10^4) events. While adding embeddings at this scale is manageable in pre-trained models, incorporating them into real-time prediction models is challenging due to both storage and inference costs. To address this scaling challenge, we propose a novel approach that leverages vector quantization (VQ) to inject a compact Semantic ID (SID) as input to the recommendation models instead of a collection of embeddings. Our method builds on recent works of SIDs by introducing three key innovations: (i) a multi-task VQ-VAE framework, called VQ fusion that fuses multiple content embeddings and categorical predictions into a single Semantic ID; (ii) a parameter-free, highly granular SID-to-embedding conversion technique, called SIDE, that is validated with two content embedding collections, thereby eliminating the need for a large parameterized lookup table; and (iii) a novel quantization method called Discrete-PCA (DPCA) which generalizes and enhances residual quantization techniques. The proposed enhancements when applied to a large-scale industrial ads-recommendation system achieves 2.4X improvement in normalized entropy (NE) gain and 3X reduction in data footprint compared to traditional SID methods.

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