Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval
Approximate Nearest Neighbour search indices form the backbone of real-world recommender systems, enabling real-time candidate retrieval over million-item catalogues. Typically, a single point estimate embedding is learnt for every user and every item. At serving time, the user embedding queries the index for relevant items. Since these representations are learnt from sparse interaction data, they are noisy and might fail to capture all the nuances that contribute to ``relevance'' -- ignoring the fundamental uncertainty that is inherent to them. The result is a retrieval pipeline that is systematically biased toward the small minority of popular head items with well-estimated embeddings, ...
| Authors | Olivier Jeunen |
|---|---|
| Primary category | cs.IR |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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