  Retrieval finds a small number of relevant candidates from a large corpus for
information retrieval and recommendation applications. A key component of
retrieval is to model (user, item) similarity, which is commonly represented as
the dot product of two learned embeddings. This formulation permits efficient
inference, commonly known as Maximum Inner Product Search (MIPS). Despite its
popularity, dot products cannot capture complex user-item interactions, which
are multifaceted and likely high rank. We hence examine non-dot-product
retrieval settings on accelerators, and propose \textit{mixture of logits}
(MoL), which models (user, item) similarity as an adaptive composition of
elementary similarity functions. This new formulation is expressive, capable of
modeling high rank (user, item) interactions, and further generalizes to the
long tail. When combined with a hierarchical retrieval strategy,
\textit{h-indexer}, we are able to scale up MoL to 100M corpus on a single GPU
with latency comparable to MIPS baselines. On public datasets, our approach
leads to uplifts of up to 77.3\% in hit rate (HR). Experiments on a large
recommendation surface at Meta showed strong metric gains and reduced
popularity bias, validating the proposed approach's performance and improved
generalization.

Machine Learning;Information Retrieval