arXiv:2607.08107v1 Announce Type: cross Abstract: Two-tower retrievers compress each user into a single embedding, limiting their ability to serve diverse interests. Multi-interest models give each user several heads scored by a maximum inner product, but their hard-routing training under-utilizes heads (routing collapse) and gives no per-user estimate of how much each interest matters for serving. We present \textbf{BACH} (\emph{Bayesian Admixture of Contrastive Heads}), which casts multi-interest two-tower retrieval as a per-user mixture over the heads, fit by variational inference. The soft
The proliferation of digital services demanding highly personalized recommendations and the limitations of existing single-embedding retrieval systems necessitate more advanced multi-interest models.
Sophisticated multi-interest retrieval models like BACH can significantly improve user experience and engagement across various platforms by better understanding and serving diverse user preferences.
Retrieval systems can now move beyond static, single-embedding user representations to dynamically model multiple, nuanced user interests, leading to more relevant recommendations and reduced 'routing collapse'.
- · E-commerce platforms
- · Content streaming services
- · Personalized advertising
- · AI/ML researchers
- · Legacy single-embedding retrieval systems
- · Platforms relying on generic user profiles
Improved relevance in recommendations and search results across online platforms.
Increased user engagement and stickiness on platforms adopting advanced multi-interest models.
Potentially more accurate and less biased understanding of user preferences, leading to new forms of personalized service delivery.
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Read at arXiv cs.LG