arxiv: 2604.13389 · v1 · submitted 2026-04-15 · 💻 cs.IR

Recognition: unknown

RoTE: Coarse-to-Fine Multi-Level Rotary Time Embedding for Sequential Recommendation

Haolin Zhang , Longtao Xiao , Guohao Cai , Ruixuan Li , Xiu Li

Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:08 UTC · model grok-4.3

classification 💻 cs.IR

keywords sequential recommendationtemporal embeddingtime span modelingmulti-level granularitytransformer modelsuser interest evolutionplug-and-play module

0 comments

The pith

RoTE improves sequential recommendation by decomposing timestamps into multi-level granularities and adding the resulting embeddings to item representations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces RoTE as a lightweight module that breaks down each interaction timestamp into several temporal levels from coarse to fine and adds these to the item embeddings used in sequence models. This addresses the common issue where only the order of interactions is considered, ignoring actual time intervals between them. A sympathetic reader would care because it could allow models to distinguish between recent and distant past behaviors more accurately, leading to better predictions of user preferences over different time horizons. The module is designed to plug into existing Transformer architectures without changes to their core structure.

Core claim

RoTE decomposes each interaction timestamp into multiple temporal granularities ranging from coarse to fine and incorporates the resulting temporal representations into item embeddings, enabling models to capture heterogeneous temporal patterns and better perceive temporal distances among user interactions during sequence modeling.

What carries the argument

The RoTE module, which uses multi-level rotary time embeddings to explicitly model time spans by decomposing timestamps into coarse-to-fine granularities and integrating them into item embeddings.

If this is right

Sequential recommendation models can be enhanced without modifying their backbone architectures.
Models gain the ability to capture both long-term and short-term interest evolution through explicit time span information.
Performance improves consistently when applied to representative Transformer-based models on public benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar multi-level time decompositions could be tested in other sequence-based tasks such as time-series forecasting or natural language processing with temporal elements.
Adaptive selection of granularity levels based on user data characteristics might further optimize the approach.
This could encourage more focus on temporal distance metrics rather than just positional encodings in recommendation systems.

Load-bearing premise

That decomposing timestamps into fixed multiple granularities and adding the embeddings will reliably capture relevant temporal patterns without overfitting or introducing new biases in the recommendation process.

What would settle it

A controlled test on datasets where time spans vary significantly but adding RoTE leads to no measurable improvement in recommendation metrics or even worse performance compared to standard positional encodings.

Figures

Figures reproduced from arXiv: 2604.13389 by Guohao Cai, Haolin Zhang, Longtao Xiao, Ruixuan Li, Xiu Li.

read the original abstract

Sequential recommendation models have been widely adopted for modeling user behavior. Existing approaches typically construct user interaction sequences by sorting items according to timestamps and then model user preferences from historical behaviors. While effective, such a process only considers the order of temporal information but overlooks the actual time spans between interactions, resulting in a coarse representation of users' temporal dynamics and limiting the model's ability to capture long-term and short-term interest evolution. To address this limitation, we propose RoTE, a novel multi-level temporal embedding module that explicitly models time span information in sequential recommendation. RoTE decomposes each interaction timestamp into multiple temporal granularities, ranging from coarse to fine, and incorporates the resulting temporal representations into item embeddings. This design enables models to capture heterogeneous temporal patterns and better perceive temporal distances among user interactions during sequence modeling. RoTE is a lightweight, plug-and-play module that can be seamlessly integrated into existing Transformer-based sequential recommendation models without modifying their backbone architectures. We apply RoTE to several representative models and conduct extensive experiments on three public benchmarks. Experimental results demonstrate that RoTE consistently enhances the corresponding backbone models, achieving up to a 20.11% improvement in NDCG@5, which confirms the effectiveness and generality of the proposed approach. Our code is available at https://github.com/XiaoLongtaoo/RoTE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

RoTE adds a multi-level rotary time embedding to sequential rec models as a plug-in module, but the gains rest on fixed granularity choices whose generalization is not yet clear from the description.

read the letter

The main point is that RoTE decomposes each timestamp into several fixed coarse-to-fine granularity levels and feeds them through rotary embeddings so the Transformer can see actual time gaps instead of just sequence order. That combination looks new in the sequential recommendation literature, even though rotary embeddings and multi-scale time features exist separately elsewhere. The paper does a clean job showing the module is lightweight, requires no backbone changes, and can be dropped into several existing models. Releasing the code is also helpful for anyone who wants to test it directly. They report consistent lifts across three public datasets, with the largest at 20.11% NDCG@5, which suggests the addition is at least not harmful and sometimes helpful in practice. The stress-test worry about fixed granularities is worth taking seriously. Because the boundaries are chosen manually rather than learned or derived from relative deltas, the embeddings could be picking up dataset-specific periodicities or simply adding capacity rather than truly modeling arbitrary time spans. Without ablations that vary the level boundaries, test on datasets with mismatched time scales, or compare against explicit interval features, it is hard to know how much of the reported gain comes from the intended mechanism. The abstract also gives no details on baselines, run counts, or statistical tests, so the strength of the empirical claim is still difficult to judge. This paper is aimed at people already working on Transformer-based sequential recommenders who need a practical way to inject real time intervals. A reader who cares about incremental engineering improvements and wants runnable code will get the most out of it. The work shows clear thinking about the temporal modeling gap and honest engagement with the usual baselines in the area. I would send it to peer review so the authors can supply the missing experimental controls and ablations; the core idea is concrete enough to be worth referee time.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes RoTE, a lightweight plug-and-play multi-level temporal embedding module for Transformer-based sequential recommendation. It decomposes each interaction timestamp into coarse-to-fine granularities and injects the resulting representations into item embeddings via rotary mechanisms, with the goal of explicitly modeling time spans to better capture heterogeneous temporal patterns and long/short-term interest evolution. The module is integrated into existing backbones without architectural changes. Experiments on three public benchmarks are reported to show consistent improvements, with a maximum gain of 20.11% in NDCG@5.

Significance. If the gains prove robust and attributable to span modeling rather than capacity or tuning artifacts, RoTE could provide a general, low-overhead way to enhance temporal awareness in sequential models. The public code release at the cited GitHub repository supports reproducibility and is a clear strength.

major comments (2)

[Method] Method section: the decomposition relies on fixed, manually selected granularity boundaries applied to absolute timestamps. No derivation or equivalence proof is given showing that rotary differences on these multi-scale features reliably encode relative time intervals (as opposed to dataset-specific periodicities or absolute-time leakage). This assumption is load-bearing for the central claim that RoTE 'explicitly models time span information' and generalizes across benchmarks.
[Experiments] Experiments section: the reported 20.11% NDCG@5 improvement and 'consistent enhancements' lack accompanying details on statistical significance testing, full baseline specifications, hyperparameter controls for added capacity, or ablations isolating the granularity levels. Without these, it is not possible to confirm that gains stem from the proposed multi-level rotary mechanism rather than confounding factors.

minor comments (2)

[Abstract] Abstract: the claim of 'extensive experiments' and specific percentage gains should be accompanied by at least the dataset names and backbone models for immediate clarity.
[Method] Notation and equations: an explicit formula for how the multi-granularity features are combined and rotated with the item embeddings would improve readability of the rotary integration step.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review. The comments on the methodological foundations and experimental validation are valuable. Below we respond point-by-point to the major comments and describe the revisions we will incorporate to strengthen the manuscript.

read point-by-point responses

Referee: [Method] Method section: the decomposition relies on fixed, manually selected granularity boundaries applied to absolute timestamps. No derivation or equivalence proof is given showing that rotary differences on these multi-scale features reliably encode relative time intervals (as opposed to dataset-specific periodicities or absolute-time leakage). This assumption is load-bearing for the central claim that RoTE 'explicitly models time span information' and generalizes across benchmarks.

Authors: We appreciate the referee drawing attention to the lack of formal justification. The granularity boundaries are selected according to standard temporal scales observed in recommendation datasets (seconds to days) to enable the model to distinguish short-term versus long-term intervals. The rotary mechanism is applied independently at each level so that angle differences at a given granularity correspond to relative time deltas at that scale, building on the relative-position property of RoPE. While the original manuscript does not contain a derivation proving equivalence to arbitrary relative intervals, the multi-level design is intended to let the attention layers learn heterogeneous span patterns rather than relying on absolute timestamps. In the revision we will expand the method section with (i) explicit motivation for the chosen boundaries, (ii) a qualitative argument showing how rotary differences at multiple resolutions capture relative spans, and (iii) an additional diagnostic experiment that visualizes embedding distances for controlled time deltas. We believe these additions will clarify the design rationale and support the central claim. revision: partial
Referee: [Experiments] Experiments section: the reported 20.11% NDCG@5 improvement and 'consistent enhancements' lack accompanying details on statistical significance testing, full baseline specifications, hyperparameter controls for added capacity, or ablations isolating the granularity levels. Without these, it is not possible to confirm that gains stem from the proposed multi-level rotary mechanism rather than confounding factors.

Authors: We agree that these controls are necessary to attribute improvements to the proposed mechanism. In the revised version we will add: (1) paired t-tests (or Wilcoxon signed-rank tests) across five random seeds for all reported metrics, (2) complete hyper-parameter tables for every baseline and RoTE variant, (3) a capacity-controlled ablation that matches the parameter count of RoTE by increasing embedding or hidden dimensions in the backbone, and (4) a granularity-level ablation that successively removes coarse-to-fine components while keeping total parameters constant. Updated tables and figures will be included. Because the code is already public, these new results can be reproduced directly. revision: yes

Circularity Check

0 steps flagged

No circularity: architectural module validated empirically, no derivation reduces to inputs

full rationale

The paper proposes RoTE, a multi-level rotary time embedding module that decomposes timestamps into coarse-to-fine granularities and injects them into item embeddings for Transformer-based sequential recommenders. Its central claim is that this design captures heterogeneous temporal patterns and improves performance, supported solely by experiments on three public benchmarks showing gains up to 20.11% NDCG@5. No equations, predictions, or first-principles results are presented that reduce by construction to fitted parameters, self-citations, or renamed inputs. The module is described as lightweight and plug-and-play, with manual granularity choices treated as design decisions rather than derived quantities. This is a standard empirical contribution with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; the module is described at the level of design motivation and empirical outcome.

pith-pipeline@v0.9.0 · 5545 in / 983 out tokens · 49135 ms · 2026-05-10T13:08:54.498338+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

32 extracted references · 6 canonical work pages · 3 internal anchors

[1]

Yongjun Chen, Jia Li, and Caiming Xiong. 2022. ELECRec: Training sequential recommenders as discriminators. InProceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 2550–2554

2022
[2]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. InProceedings of the 10th ACM conference on recommender systems. 191–198

2016
[3]

Elliott Hauser. 2018. UNIX Time, UTC, and datetime: Jussivity, prolepsis, and in- corrigibility in modern timekeeping.Proceedings of the Association for Information Science and Technology55, 1 (2018), 161–170

2018
[4]

Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In2016 IEEE 16th international conference on data mining (ICDM). IEEE, 191–200

2016
[5]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk
[6]

Session-based recommendations with recurrent neural networks.arXiv preprint arXiv:1511.06939(2015)

work page internal anchor Pith review arXiv 2015
[7]

Min Hou, Le Wu, Yuxin Liao, Yonghui Yang, Zhen Zhang, Changlong Zheng, Han Wu, and Richang Hong. 2025. A survey on generative recommendation: Data, model, and tasks.arXiv preprint arXiv:2510.27157(2025)

work page internal anchor Pith review arXiv 2025
[8]

Yupeng Hou, Zhankui He, Julian McAuley, and Wayne Xin Zhao. 2023. Learning vector-quantized item representation for transferable sequential recommenders. InProceedings of the ACM Web Conference 2023. 1162–1171

2023
[9]

Yupeng Hou, Jiacheng Li, Ashley Shin, Jinsung Jeon, Abhishek Santhanam, Wei Shao, Kaveh Hassani, Ning Yao, and Julian McAuley. 2025. Generating long semantic ids in parallel for recommendation. InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2. 956–966

2025
[10]

Yupeng Hou, An Zhang, Leheng Sheng, Zhengyi Yang, Xiang Wang, Tat-Seng Chua, and Julian McAuley. 2025. Generative Recommendation Models: Progress and Directions. InCompanion Proceedings of the ACM Web Conference 2025

2025
[11]

Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023. How to index item ids for recommendation foundation models. InProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region. 195–204

2023
[12]

Zan Huang. 2025. Revisiting Self-Attentive Sequential Recommendation.CoRR abs/2504.09596 (2025). arXiv:2504.09596 https://arxiv.org/abs/2504.09596

work page arXiv 2025
[13]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recom- mendation. In2018 IEEE international conference on data mining (ICDM). IEEE, 197–206

2018
[14]

Sara Latifi, Dietmar Jannach, and Andrés Ferraro. 2022. Sequential recommenda- tion: A study on transformers, nearest neighbors and sampled metrics.Informa- tion Sciences609 (2022), 660–678

2022
[15]

Alejo Lopez-Avila, Jinhua Du, Abbas Shimary, and Ze Li. 2024. Positional encod- ing is not the same as context: A study on positional encoding for Sequential recommendation.arXiv preprint arXiv:2405.10436(2024)

work page arXiv 2024
[16]

Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel
[17]

InProceedings of the 38th international ACM SIGIR conference on research and development in information retrieval

Image-based recommendations on styles and substitutes. InProceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 43–52
[18]

Mahreen Nasir and Christie I Ezeife. 2023. A survey and taxonomy of sequential recommender systems for e-commerce product recommendation.SN Computer Science4, 6 (2023), 708

2023
[19]

Li-Wei Pan, Wei-Ke Pan, Mei-Yan Wei, Hong-Zhi Yin, and Zhong Ming. 2026. A survey on sequential recommendation.Frontiers of Computer Science20, 3 (2026), 2003606

2026
[20]

Aleksandr V Petrov and Craig Macdonald. 2023. Generative sequential recom- mendation with gptrec.arXiv preprint arXiv:2306.11114(2023)

work page arXiv 2023
[21]

Ernst Pöppel. 1997. A hierarchical model of temporal perception.Trends in cognitive sciences1, 2 (1997), 56–61

1997
[22]

Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al
[23]

Recommender systems with generative retrieval.Advances in Neural Information Processing Systems36 (2023), 10299–10315

2023
[24]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factor- izing personalized markov chains for next-basket recommendation. InProceedings of the 19th international conference on World wide web. 811–820

2010
[25]

Syed Tauhid Ullah Shah, Fazlullah Khan, Shirin Yamani, Ryan Alturki, Foziah Gazzawe, and Muhammad Imran Razzak. 2025. DSRS: DELIGHT sequential recommender system.Engineering Applications of Artificial Intelligence142 (2025), 109936

2025
[26]

Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing568 (2024), 127063

2024
[27]

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang
[28]

InProceedings of the 28th ACM international conference on information and knowledge management

BERT4Rec: Sequential recommendation with bidirectional encoder rep- resentations from transformer. InProceedings of the 28th ACM international conference on information and knowledge management. 1441–1450
[29]

Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommenda- tion via convolutional sequence embedding. InProceedings of the eleventh ACM international conference on web search and data mining. 565–573

2018
[30]

Peiyang Wei, Hongping Shu, Jianhong Gan, Xun Deng, Yi Liu, Wenying Sun, Tinghui Chen, Can Hu, Zhenzhen Hu, Yonghong Deng, et al. 2025. Sequential recommendation system based on deep learning: A survey.Electronics14, 11 (2025), 2134

2025
[31]

Longtao Xiao, Haozhao Wang, Cheng Wang, Linfei Ji, Yifan Wang, Jieming Zhu, Zhenhua Dong, Rui Zhang, and Ruixuan Li. 2025. Unger: Generative recommendation with a unified code via semantic and collaborative integration. ACM Transactions on Information Systems44, 2 (2025), 1–31

2025
[32]

Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhao- jie Gong, Fangda Gu, Michael He, et al. 2024. Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations.arXiv preprint arXiv:2402.17152(2024)

work page internal anchor Pith review arXiv 2024