Title: TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Sequential Recommendation

Graph Signal Processing
Abstract: Recently, convolutional filters have been increasingly adopted in sequential recommendation for their ability to capture local sequential patterns. However, most of these models complement convolutional filters with self-attention. This is because convolutional filters alone, generally fixed filters, struggle to capture global interactions necessary for accurate recommendation. We propose \textbf{T}ime-\textbf{V}ariant Convolutional Filters for Sequential \textbf{Rec}ommendation (TV-Rec), a model inspired by graph signal processing, where time-variant graph filters capture position-dependent temporal variations in user sequences. By replacing both fixed kernels and self-attention with time-variant filters, TV-Rec achieves higher expressive power and better captures complex interaction patterns in user behavior. This design not only eliminates the need for self-attention but also reduces computation while accelerating inference. Extensive experiments on six public benchmarks show that TV-Rec outperforms state-of-the-art baselines by an average of 7.49\%.


Peer reviews:
comments: SUMMARY:
The paper proposes a new technique to allow sequential recommendation models to more flexibly adapt to different temporal patterns. Existing sequential models are fairly "rigid" (just an ordered sequence of events), whereas the proposed approach can potentially be more adaptive to context and different dynamics at different positions in the sequence. The specific contribution is based on a time-varying convolutional filter.

STRENGTHS AND WEAKNESSES:
-- Reasonable idea which seems to offer a unique perspective on a problem that's studied in a large number of papers.

-- Fairly promising results.

-- A decent enough set of baselines and datasets, to the point that the experiments are reasonably thorough.

-- Datasets maybe on the small side, not sure about scalability.

QUESTIONS:
-- Can you comment on model scalibility? I see there is some scalability analysis, though the method seems not to have been evaluated on large datasets.

-- The results in the scalability table don't seem to match the numbers in the table (FMLPRec seems to be the winner in the figure?)

-- I don't totally follow what's shown in the inference time analysis. Many of these methods reduce inference to nearest-neighbor search in some latent space such that I don't really see why inference time should vary so much (though maybe that's sort of what the table shows). Though might have misunderstood something here.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-05

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes TV-Rec, a sequential recommendation model that leverages graph signal processing to perform spectral filtering on user behavior sequences. By constructing time-variant filters for each position via a low-rank formulation, the model avoids using position embeddings or attention. Experimental results on six datasets show strong performance and efficiency compared to prior methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper introduces a novel sequential recommendation approach using spectral graph signal processing (GSP). Its key innovation is time-variant convolutional filters assigning unique spectral filters per position, replacing position embeddings and self-attention with an efficient low-rank factorization.\n2. Experiments on six benchmarks show consistent gains over 10 state-of-the-art models, supported by ablation studies and efficiency analysis.\n3. The approach offers a compelling alternative to the dominant Transformer-based models, which often require heavy computation and explicit position encoding.\n\nWeaknesses:\n1. Key design choices such as the number of basis vectors m, shift depth K, or basis configuration ω are not systematically analyzed. It remains unclear whether the method is robust under different settings.\n2. The basis matrix \\bar{B} is constructed heuristically via Vandermonde form, but the selection of frequency anchors and the number of basis vectors lack theoretical or empirical justification. Please explain the rationale behind their choice in the main paper.\n3. Strong recent models like Tim4Rec are not included in the comparison. Their absence weakens the empirical competitiveness of the claims.\n\nQUESTIONS:\n1.\tWhat is the theoretical expressive capacity of the filter design?\n\nCan the authors formalize whether the C \\cdot \\bar{B} construction is capable of approximating arbitrary frequency responses under certain norms or graph structures?\n\n2.\tCan personalization be incorporated into filters?\n\nCurrently, all filters are position-specific but shared across users. Could user- or item-aware adaptation (e.g., via embeddings or gating) enhance personalization, especially in cold-start scenarios?\n\n3.\tHow does TV-Rec handle data sparsity or cold-start scenarios? \n\nGiven its dependence on spectral structures, how robust is the model when faced with sparse user interactions or new users/items with limited historical data, which are common challenges in real-world recommendation systems?\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces TV-Rec, a novel architecture for sequential recommendation that leverages time-variant convolutional filters inspired by graph signal processing. These filters adapt dynamically to different temporal positions in a user’s interaction sequence, in contrast to traditional fixed convolutional filters or self-attention mechanisms. TV-Rec eliminates the need for self-attention and positional embeddings while providing better performance and computational efficiency. The model is evaluated on six benchmark datasets and consistently outperforms ten state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**\n\n- The paper is well-written and clearly structured. The motivation is well-articulated, and Table 1 effectively contextualizes the proposed method among existing approaches.\n- The concept of a time-aware (time-variant) convolutional filter is novel and intuitively meaningful.\n- The method is grounded in solid theoretical foundations, with a well-developed formulation based on graph signal processing.\n- The interpretability of TV-Rec is enhanced by filter behavior analysis and an illustrative case study.\n- The experimental evaluation is thorough, covering both qualitative and quantitative aspects. The authors also include comprehensive hyperparameter analysis in the appendix.\n\n### **Weaknesses**\n\n- While the paper focuses on leveraging GSP for sequential recommendation, the benefits of this framework are not thoroughly connected to the SR context. For instance, in Lines 106–109, the statement that GSP helps \"capture complex dependencies\" is too vague. The paper would benefit from a more precise explanation of how GSP-derived filters concretely improve sequential modeling.\n- The decision to model user sequences as a line graph is not well-justified. Why choose a line graph over other graph structures? How sensitive is the performance to the graph construction method? This should be discussed more explicitly.\n- Given that TV-Rec uses graph-based modeling, it would be helpful to relate and compare this work to existing GNN-based recommendation methods. Even a brief discussion or experimental comparison would strengthen the paper.\n- Although the authors provide mean and standard deviation of experimental results in the appendix, it would improve transparency to report p-values or statistical significance directly in the main text.\n\nQUESTIONS:\n- In Eq. (13), what do $B_{\\text{real}}$ and $B_{\\text{imag}}$ represent, and why is orthogonal regularization applied to these components?\n- In Fig. 3a, how is the basic graph filter learned? Is it derived from a baseline method or from TV-Rec with static (non-time-variant) filters?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis article highlights the limitations of traditional convolutional filters in sequential recommendation tasks due to their fixed nature. To tackle this, the authors propose TV-Rec, a novel method that employs time-variant convolutional filters. These filters adaptively apply different filters to each data point, allowing for the capture of complex patterns. A key advantage of TV-Rec is its ability to achieve high performance in long-range modeling without the need for self-attention. Additionally, it boasts fast inference times due to its linear operator nature. The effectiveness and efficiency of TV-Rec have been rigorously validated through extensive experiments on six datasets.\n\nSTRENGTHS AND WEAKNESSES:\nTV-Rec addresses the inherent limitations of conventional fixed convolutional filters in super-resolution by introducing time-variant filters that dynamically adapt to each data point, enabling the capture of complex patterns without relying on self-attention mechanisms. This approach not only achieves high performance in long-range modeling but also maintains fast inference speeds due to its linear operator nature, as validated through extensive experiments across six datasets, demonstrating both superior effectiveness and practical efficiency.\n\n\nA major drawback of the paper is its insufficient explanation of the practical implementation of the time-variant filter: while it cites graph signal processing concepts and provides mathematical formulations (e.g., filter matrix \\(H\\) and frequency-domain operations), the main text lacks a clear, step-by-step description of how the filter dynamically adapts per data point during inference, how the graph Fourier transforms interact with filter taps, or how parameters are learned during training. This omission hinders readability and reproducibility, particularly for readers unfamiliar with graph signal processing.\n\nA critical omission in the paper is its failure to benchmark TV-Rec against existing graph-based sequential recommendation models (e.g., GNNs or graph convolutional approaches), despite its reliance on graph signal processing principles. This absence of comparison prevents readers from evaluating TV-Rec's relative performance and novelty within the broader graph-driven sequential recommendation literature, thereby undermining its contextual significance.\n\nThe paper suffers from overly terse table and figure captions (e.g., \"Ablation studies\" for Table 4 and \"Visualization of learned graph filters on LastFM\" for Figure 3), which omit critical context about experimental objectives, variables, and key insights. This lack of detail forces readers to deduce significance from the main text, diminishing the work's clarity and professional presentation.\n\nThe paper’s casual use of ambiguous abbreviations (e.g., \"SR\" for sequential recommendation without explicit definition) risks confusion, particularly for readers unfamiliar with the field. Moreover, Section 4.4 fails to provide proper citations for compared methods (AdaMCT, BSARec, SASRec, BERT4Rec, FMLPRec), hindering source verification and weakening the academic rigor of the complexity and runtime analysis.\n\nQUESTIONS:\n1. Given that TV-Rec is built on graph signal processing principles, why does the paper not include mainstream graph-based sequential recommendation models (e.g., those leveraging graph neural networks or graph convolutional networks) as baselines in its experimental evaluations? \n \n2. Without comparisons to such graph-based models, how can readers assess whether TV-Rec offers significant advantages or uniqueness in capturing sequential dependencies compared to existing graph convolutional approaches?  \n\n3. The paper claims the time-variant graph filter is a core innovation, but does it include dedicated ablation studies that directly compare this filter with standard graph convolution operations under the same experimental settings to explicitly demonstrate the former’s superiority?  In the absence of such comparative ablation experiments, how can the claim that the time-variant graph filter is a more effective design choice than traditional graph convolution for modeling the dynamics and complexity of user sequences be validated?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}