Title: HPSERec: A Hierarchical Partitioning and Stepwise Enhancement Framework for Long-tailed Sequential Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Sequential Recommendation; Long-tail Problem; Transfer Learning
Abstract: The long-tail problem in sequential recommender systems stems from imbalanced interaction data, resulting in suboptimal model performance for tail users and items. Recent studies have leveraged head data to enhance tail data for diminish the impact of the long-tail problem. However, these methods often adopt ad-hoc strategies to distinguish between head and tail data, which fails to capture the underlying distributional characteristics and structural properties of each category. Moreover, due to a substantial representational gap exists between head and tail data, head-to-tail enhancement strategies are susceptible to negative transfer, often leading to a decline in overall model performance. To address these issues, we propose a hierarchical partitioning and stepwise enhancement framework, called HPSERec, for long-tailed sequential recommendation. HPSERec partitions the item set into subsets based on a data imbalance metric, assigning an expert network to each subset to capture user-specific local features. Subsequently, we apply knowledge distillation to progressively improve long-tail interest representation, followed by a Sinkhorn optimal transport-based feedback module, which aligns user representations across expert levels through a globally optimal and softly matched mapping. Extensive experiments on three real-world datasets demonstrate that HPSERec consistently outperforms all baseline methods. The implementation code is available at https://anonymous.4open.science/r/HPSERec-2404.


Peer reviews:
comments: SUMMARY:
This paper investigates the sequential recommendation problem for long-tail items and proposes a stepwise enhancement approach. Specifically, it improves recommendation performance for long-tail items by progressively transferring knowledge among experts trained on different item groups.

STRENGTHS AND WEAKNESSES:
Strengths:
1.	The proposed idea of stepwise training experts at different levels is innovative, and its training methodology has broad generalizability.
2.	The appendix provides a theoretical proof for the feedback module.
3.	Experiments demonstrate significant performance improvements with the proposed method.

Weaknesses:
1.	The algorithm incurs higher computational costs. Beyond the notable cost of partitioning item groups, training experts also involves substantial overhead, which was not evaluated in the paper.
2.	The paper’s clarity could be improved. Some formulas are ambiguously expressed, including undefined symbols and a lack of explanations (e.g., Appendix Formula 18). Additionally, Equation (14) mentioned in the main text is not visible.
3.	The appendix (Section A.2) does not clearly explain the differences between the provided proof and the standard Sinkhorn algorithm.

QUESTIONS:
1.	What is the computational cost introduced by training experts? Could this be theoretically analyzed?
2.	Does the entire training process guarantee convergence? How is convergence determined?
3.	Does the stepwise data-increment method for training experts ensure diverse data distribution across different experts?
4.	Which expert does the model rely on for its final output?

LIMITATIONS:
Limitations are not fully addressed. In order to improve the paper, some other ways to generate the sub-datasets to train different experts can be added.
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-18

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents HPSERec, a framework for long-tailed sequential recommendation that mitigates item popularity bias. It features: (1) hierarchical item partitioning via a dynamic imbalance metric; (2) stepwise expert training with knowledge distillation; and (3) a feedback module using Sinkhorn optimal transport for global alignment. Experiments on three datasets demonstrate superior tail-item performance over traditional and LLM-based baselines without sacrificing head-item accuracy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This work proposes a hierarchical dynamic partitioning strategy that utilizes a data imbalance metric and dynamic programming algorithm to replace traditional fixed-ratio partitioning methods, demonstrating superior adaptability to varying skewed distributions across different datasets.\n2. The stepwise enhancement approach progressively improves tail item representations through a Mixture-of-Experts network architecture, combined with knowledge distillation, effectively mitigating the issue of negative transfer in representation learning.\n3. The study makes a theoretical contribution by being the first to decompose the long-tail problem into multi-level subtasks, presenting a comprehensive framework that integrates metric design, partitioning, distillation, and alignment.\n\nWeaknesses:\n1. The dynamic programming-based partitioning algorithm exhibit computational inefficiency when applied to extremely large-scale item sets, requiring further optimization or approximation methods for practical deployment.\n2. While the method indirectly improves performance for tail users through item representation enhancement, the lack of dedicated modules specifically addressing user-side sparsity could potentially limit its effectiveness for extreme cold-start user scenarios.\n3. The alternating training strategy significantly increases training complexity, potentially affecting the method's scalability and practical implementation efficiency.\n\nQUESTIONS:\n1. The proposed method trains user interests starting from tail interests in the feedforward module until complete user interests are learned. However, is the reverse approach more reasonable?\n2. Why does this paper not include comparisons with classical long-tail recommendation methods, such as LightGCN?\n3. Is there a potential error in the definition of data imbalance (Equation 5)? Could the authors clarify the meanings of the two exponential terms (α and 1/(1−α)) separately?\n4. While the paper primarily addresses the long-tail distribution of known items, how does the proposed method perform on entirely cold-start items?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes HPSERec, a method that splits items into balanced groups and uses stepwise knowledge transfer to better recommend rare (tail) items. It improves long-tail recommendation accuracy while keeping overall performance strong.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n1. Clear motivation, focusing on a problem of practical significance.\n2. Good Writing.\n3. The method is reasonable and provides theoretical support.\n\nCons:\n1. Regarding the division of head and tail, it seems that for different data sets, a simpler strategy can be used to divide them through statistical information. The division method proposed by the author is reasonable, but the necessity needs further discussion.\n2. Regarding the grouping method, there is also a lack of comparative experiments with some naive strategy groupings.\n3. The author focuses on the long-tail problem, but in the selection of baselines, there are not enough methods that focus on the long-tail problem for comparison.\n4. The experiment in Figure 4 is difficult to understand, and the correspondence between the two axes and the figure should be more clearly stated. At the same time, it seems that the performance trends of different methods in different groups are consistent, which does not seem to clearly show the advantages of the author's method for long-tail problems.\n5. The method is relatively complex, and it is recommended to add experiments to demonstrate its scalability.\n\nQUESTIONS:\nRegarding the division of head and tail, it seems that for different data sets, a simpler strategy can be used to divide them through statistical information. The division method proposed by the author is reasonable, but is it necessary? Could the authors provide comparative experiments using simpler or naive grouping strategies to better demonstrate the necessity and effectiveness of their proposed partitioning approach?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces HPSERec (Hierarchical Progressive Stepwise Enhancement for Long-Tail Recommendation), a novel framework designed to address the long-tail problem in sequential recommendation systems. HPSERec integrates hierarchical dynamic partitioning, progressive knowledge distillation, and optimal transport-based feedback alignment to improve the modeling of sparse and imbalanced user-item interactions. By leveraging a data-driven distribution balancing module and multi-level expert networks, the framework adapts to varying degrees of item popularity while avoiding negative transfer. Unlike LLM-enhanced approaches, HPSERec achieves robust performance without relying on large language models, making it more efficient and scalable for real-world deployment.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The work proposes a principled hierarchical decomposition of long-tail distributions, moving beyond simplistic head/tail dichotomy to provide a more granular and theoretically grounded approach to sparsity mitigation in recommendation systems.\n\n2. A novel optimal transport-assisted feedback alignment mechanism is introduced, which leverages Sinkhorn-based distribution matching to theoretically guarantee feature consistency across different hierarchical subsets, ensuring more robust knowledge transfer.\n\n3. Comprehensive experimental validation across multiple benchmark datasets demonstrates the framework's effectiveness, while rigorous ablation studies provide valuable insights into the contribution of individual components to the overall performance.\n\n4. Compared to LLM-dependent approaches like RLMRec, HPSERec achieves comparable performance while operating within traditional recommendation architectures, making it significantly more scalable and practical for real-world deployment.\n\nWeaknesses:\n\n1. The cascaded computation in the multi-layer MoE (Mixture of Experts) architecture increases both memory consumption and inference latency, which potentially impact the real-time responsiveness requirements of online recommendation services.\n\n2. The theoretical foundation of the optimal transport component assumes ideal uniform distributions, an assumption that does not fully align with real-world user behavior data that often exhibits strong clustering effects and complex distribution patterns.\n\nQUESTIONS:\n1. The authors note in the appendix that the proposed method is only suitable for small-scale datasets, but fail to clarify its potential industrial application scenarios. Could the authors elaborate on how this approach might be adapted or scaled for real-world industrial deployment?\n\n2. Although the proposed alternating training strategy—optimizing downstream experts during the feedforward moduleand fine-tuning upstream experts in the feedback module—significantly improves model performance, does the backpropagation-based update of upstream expert parameters during the feedback module risk disrupting the already stabilized parameters learned in the feedforward module?\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the long-tail problem in Sequential Recommendation Systems (SRS) by proposing HPSERec, a framework combining (1) an imbalance-aware hierarchical partitioning of items, (2) a feedforward knowledge distillation mechanism to enhance tail item representations, and (3) a feedback module using Sinkhorn-based optimal transport to align user representations across different expert levels. The framework aims to boost the performance on tail items and users while maintaining recommendation quality for head items. Extensive experiments on three real-world datasets show that HPSERec consistently outperforms both traditional and large language model-enhanced baselines in overall performance and long-tail recommendation accuracy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The hierarchical design with imbalance-based partitioning, stepwise knowledge transfer via distillation, and global alignment via optimal transport is novel, well-integrated, and mathematically sound, with clear theoretical grounding.\n\n- The proposed method shows consistent improvements over competitive baselines, including recent LLM-based methods, across multiple datasets and scenarios (tail users/items, overall ranking), with solid ablations validating each module’s contribution.\n\nWeaknesses\n\n- While the proposed distribution balancing algorithm improves over fixed-ratio partitioning, it still assumes static, discrete partitions of items and users. The method does not address temporal dynamics, gradual shifts in item popularity, or cross-group interactions, which are common in real-world recommendation scenarios. This limits the generalizability of the approach beyond the datasets and static environments tested.\n\n- The use of multiple expert models, stepwise distillation, and computationally expensive Sinkhorn optimal transport introduces non-negligible computational overhead. The paper lacks a detailed analysis of training and inference efficiency, particularly on large-scale datasets or in real-time scenarios.\n\nQUESTIONS:\n- How sensitive is the performance of HPSERec to the number of partitions (L)? Have you explored adaptive or learned partitioning beyond static imbalance-based splitting?\n\nLIMITATIONS:\nThe key limitations are summarized in the weaknesses section above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-07"}