Keeping the Index Open: The Recommendation-Side Cost of Shared Search and Recommendation

A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering both surfaces over one catalog, 38.6% of held-out query-search impressions show an item never previously shown or visited. For user-cold engagements, the feature-based tower serves this demand without measurable loss against 99 sampled negatives (0.9595 Recall@20 versus 0.9510 warm). A lexical baseline reaches similar parity, while a full-catalog check remains statistically undecided. Dual-encoder retrieval therefore keeps the index open to new items, unlike an ID-softmax recommender that requires retraining. We price this openness on recommendation against six sequential baselines, each retrained and tuned through five rounds on corrected targets. A float32 timestamp bug had reordered leave-one-out targets for 19.7% of users. On MovieLens-1M, warm accuracy trails the strongest retrained baseline by 5.2% Recall@20 and 11.4% NDCG@20. On MIND, the gap narrows to 0.8--3.6% relative to the five strongest baselines, though the model ranks sixth of seven. Under strict zero-leakage cold-start evaluation, the content tower achieves 0.172 ± 0.006 Recall@20, 1.4× the strongest retrained dedicated method (0.124 ± 0.007) and 3× a training-free floor, without cold-specific training. Exact full-softmax training raises Recall@20 by 54% on MIND-small and 6.9% on MovieLens-1M over sampled InfoNCE, but recomputes the full catalog each step and exhausts accelerator memory at 240K items. Approximate nearest-neighbor search explains none of the remaining gap, serving cost does not regress against ID-softmax retrieval, and a history-window sweep explains half the post-recipe remainder. Exact-quality training at catalog scale remains the open problem.