Auto-regressive next-token prediction is strictly equivalent to full-vocabulary maximum likelihood estimation in generative recommendation under bijective item-to-token-sequence mapping.
Farewell to item ids: Unlocking the scaling potential of large ranking models via semantic tokens
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
UxSID models ultra-long user sequences with semantic-group shared interest memory using Semantic IDs and dual-level attention, achieving state-of-the-art performance and a 0.337% revenue lift in advertising A/B tests.
SIF encodes entire historical raw samples as tokens via hierarchical group-adaptive quantization and token/sample-level mixing to overcome partial encoding and feature heterogeneity limits in scaled recommender models.
citing papers explorer
-
On the Equivalence Between Auto-Regressive Next Token Prediction and Full-Item-Vocabulary Maximum Likelihood Estimation in Generative Recommendation--A Short Note
Auto-regressive next-token prediction is strictly equivalent to full-vocabulary maximum likelihood estimation in generative recommendation under bijective item-to-token-sequence mapping.
-
UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
UxSID models ultra-long user sequences with semantic-group shared interest memory using Semantic IDs and dual-level attention, achieving state-of-the-art performance and a 0.337% revenue lift in advertising A/B tests.
-
Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models
SIF encodes entire historical raw samples as tokens via hierarchical group-adaptive quantization and token/sample-level mixing to overcome partial encoding and feature heterogeneity limits in scaled recommender models.