Token-Weighted Multi-Target Learning for Generative Recommenders with Curriculum Learning

• URL: http://arxiv.org/abs/2601.17787v1
• Date: Sun, 25 Jan 2026 11:01:14 GMT
• Title: Token-Weighted Multi-Target Learning for Generative Recommenders with Curriculum Learning
• Authors: Wei-Ning Chiu, Chuan-Ju Wang, Pu-Jen Cheng,
• Abstract summary: We propose two complementary information-gain-based token-weighting strategies tailored to generative recommendation with semantic IDs.<n>We introduce a multi-target learning framework with curriculum learning that jointly optimize the two token-weighted objectives alongside standard likelihood.
• Score: 8.152671524863083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative recommender systems have recently attracted attention by formulating next-item prediction as an autoregressive sequence generation task. However, most existing methods optimize standard next-token likelihood and implicitly treat all tokens as equally informative, which is misaligned with semantic-ID-based generation. Accordingly, we propose two complementary information-gain-based token-weighting strategies tailored to generative recommendation with semantic IDs. Front-Greater Weighting captures conditional semantic information gain by prioritizing early tokens that most effectively reduce candidate-item uncertainty given their prefixes and encode coarse semantics. Frequency Weighting models marginal information gain under long-tailed item and token distributions, upweighting rare tokens to counteract popularity bias. Beyond individual strategies, we introduce a multi-target learning framework with curriculum learning that jointly optimizes the two token-weighted objectives alongside standard likelihood, enabling stable optimization and adaptive emphasis across training stages. Extensive experiments on benchmark datasets show that our method consistently outperforms strong baselines and existing token-weighting approaches, with improved robustness, strong generalization across different semantic-ID constructions, and substantial gains on both head and tail items. Code is available at https://github.com/CHIUWEINING/Token-Weighted-Multi-Target-Learning-for-Generative-Recommenders-with -Curriculum-Learning.

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