TAAL Fixes Early Beam Pruning in Generative Recommendation
_reachsumit · x · 2026-09-01
Paper reveals that >90% of retrieval failures in generative recommendation happen in the first two decoding steps, proposing TAAL to fix this.
Core Issue: Standard next-token prediction doesn't cover multimodal transitions, causing the correct SID to be pruned irreversibly in early beam search.
Solution:
- Training: Constructs joint soft targets from historical transitions and aligns early-prefix distribution with forward KL.
- Inference: Calibrates candidate scores using Pointwise Mutual Information (PMI) to reduce global high-frequency prefix influence.
Results: On Amazon Beauty, Instruments, and Yelp, NDCG@10 improved by 39.5%, 6.7%, and 28.6% respectively; Full-SID survival increased by 3.9%-16.6%.
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