MaLaCo: Masked Latent Completion for Efficient Generative Recommendation

Semantic-ID (SID) recommenders retrieve items by generating short discrete identifiers, but masked-diffusion decoding repeatedly processes every beam hypothesis through the full history backbone. We introduce masked latent completion (MaLaCo), which factorizes retrieval into a single bidirectional history-completion pass followed by trie-constrained SID generation with a small autoregressive head. Beam-dependent computation is therefore confined to the head. On one A100, at beam width 50, MaLaCo reduces amortized generation cost from $13.097$ to $0.526$ ms/query at batch $20$ ($24.9\times$) and single-request latency from $24.453$ to $9.984$ ms ($2.4\times$), despite a higher total parameter count relative to LLaDA-Rec, a leading generative retrieval baseline. Across four datasets, MaLaCo improves Recall@$k$ over LLaDA-Rec at every reported cutoff \(k\geq10\), with larger gains at deeper cutoffs. It trades Recall@1 for stronger deep recall on Industrial & Scientific, Musical Instruments, and Video Games, while also improving Recall@1 on Arts, Crafts & Sewing. A four-seed \(2\times2\) architecture–encoder study confirms that this depth-dependent advantage persists under either item encoder, while the stronger encoder consistently benefits MaLaCo. We further introduce a collision-aware SID construction pipeline combining balanced residual quantization, last-level deduplication, learned candidate refinement, and catalog-level assignment. Across all four catalogs, it reduces the excess-collision rate from \(18.12%\)–\(31.04%\) to at most \(0.0122%\). On an item-arrival partition of Arts, the method inserts the newest quarter of the catalog without changing existing identifiers or introducing new collisions. After recommender retraining, the extended vocabulary obtains \(0.04526\) Recall@10 versus \(0.04598\) after complete vocabulary refitting, a $1.6\%$ relative shortfall. Together, these results position MaLaCo as an efficient, high-recall candidate generator with collision-aware and stably extensible semantic identifiers.