arXiv:2512.10388v3 Announce Type: replace-cross Abstract: 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 s
The increasing complexity and scale of recommender systems, particularly with long-tail items, are driving innovation in how item embeddings are constructed and utilized to improve recommendation quality.
Improving the accuracy and robustness of sequential recommender systems directly impacts e-commerce, content platforms, and advertising, enabling more personalized experiences and potentially increasing user engagement and revenue.
New methods for harmonizing semantic and hash IDs could lead to more robust and less vulnerable recommender systems, especially in scenarios with scarce interaction data, shifting the paradigm for item embedding strategies.
- · E-commerce platforms
- · Content streaming services
- · Advertising technology companies
- · AI/ML researchers in recommendation systems
- · Legacy recommender systems reliant solely on hash IDs
- · Platforms with significant long-tail item challenges
More accurate and resilient sequential recommendation models emerge, enhancing user experience on various platforms.
Increased user satisfaction and engagement could drive higher conversion rates and retention for businesses utilizing these advanced systems.
The broader adoption of hybrid ID approaches might influence the design of future data infrastructure for handling diverse item representations in AI applications.
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Read at arXiv cs.AI