@inproceedings{wei-etal-2026-past,
title = "From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation",
author = "Wei, Kaiwen and
he, Kejun and
Kang, Xiaomian and
Zhang, Jie and
Ymyang and
Jin, Li and
Li, Zhenyang and
Zhong, Jiang and
Bai, Richard He and
Zhu, Junnan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.475/",
pages = "10421--10441",
ISBN = "979-8-89176-390-6",
abstract = "Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However, this left-to-right paradigm inherently biases the model towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents.To address this limitation, we propose Masked History Learning (MHL), a novel training framework that shifts the objective from simple next-step prediction to deep comprehension of history. MHL augments the standard autoregressive objective with an auxiliary task of reconstructing masked historical items, compelling the model to understand ``why'' an item path is formed from the user{'}s past behaviors, rather than just ``what'' item comes next.We introduce two key contributions to enhance this framework: (1) an entropy-guided masking policy that intelligently targets the most informative historical items for reconstruction, and (2) a curriculum learning scheduler that progressively transitions from history reconstruction to future prediction.Experiments on three public datasets show that our method significantly outperforms state-of-the-art generative models, highlighting that a comprehensive understanding of the past is crucial for accurately predicting a user{'}s future path. The code is available at https://github.com/CQU-MM-Intelligent-Lab/MHL."
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<abstract>Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However, this left-to-right paradigm inherently biases the model towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents.To address this limitation, we propose Masked History Learning (MHL), a novel training framework that shifts the objective from simple next-step prediction to deep comprehension of history. MHL augments the standard autoregressive objective with an auxiliary task of reconstructing masked historical items, compelling the model to understand “why” an item path is formed from the user’s past behaviors, rather than just “what” item comes next.We introduce two key contributions to enhance this framework: (1) an entropy-guided masking policy that intelligently targets the most informative historical items for reconstruction, and (2) a curriculum learning scheduler that progressively transitions from history reconstruction to future prediction.Experiments on three public datasets show that our method significantly outperforms state-of-the-art generative models, highlighting that a comprehensive understanding of the past is crucial for accurately predicting a user’s future path. The code is available at https://github.com/CQU-MM-Intelligent-Lab/MHL.</abstract>
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%0 Conference Proceedings
%T From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation
%A Wei, Kaiwen
%A he, Kejun
%A Kang, Xiaomian
%A Zhang, Jie
%A Jin, Li
%A Li, Zhenyang
%A Zhong, Jiang
%A Bai, Richard He
%A Zhu, Junnan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Ymyang
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wei-etal-2026-past
%X Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However, this left-to-right paradigm inherently biases the model towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents.To address this limitation, we propose Masked History Learning (MHL), a novel training framework that shifts the objective from simple next-step prediction to deep comprehension of history. MHL augments the standard autoregressive objective with an auxiliary task of reconstructing masked historical items, compelling the model to understand “why” an item path is formed from the user’s past behaviors, rather than just “what” item comes next.We introduce two key contributions to enhance this framework: (1) an entropy-guided masking policy that intelligently targets the most informative historical items for reconstruction, and (2) a curriculum learning scheduler that progressively transitions from history reconstruction to future prediction.Experiments on three public datasets show that our method significantly outperforms state-of-the-art generative models, highlighting that a comprehensive understanding of the past is crucial for accurately predicting a user’s future path. The code is available at https://github.com/CQU-MM-Intelligent-Lab/MHL.
%U https://aclanthology.org/2026.acl-long.475/
%P 10421-10441
Markdown (Informal)
[From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation](https://aclanthology.org/2026.acl-long.475/) (Wei et al., ACL 2026)
ACL
- Kaiwen Wei, Kejun he, Xiaomian Kang, Jie Zhang, Ymyang, Li Jin, Zhenyang Li, Jiang Zhong, Richard He Bai, and Junnan Zhu. 2026. From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10421–10441, San Diego, California, United States. Association for Computational Linguistics.