Title: Path-Enhanced Contrastive Learning for Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: graph mining

recommender systems

self-supervised learning

Contrastive Learning

Data Augmentation
Abstract: Collaborative filtering (CF) methods are now facing the challenge of data sparsity in recommender systems. In order to reduce the effect of data sparsity, researchers proposed contrastive learning methods to extract self-supervised signals from raw data. Contrastive learning methods address this problem by graph augmentation and maximizing the consistency of node representations between different augmented graphs. However, these methods tends to unintentionally distance the target node from its path nodes on the interaction path, thus limiting its effectiveness. In this regard, we propose a solution that uses paths as samples in the contrastive loss function. In order to obtain the path samples, we design a path sampling method. In addition to the contrast of the relationship between the target node and the nodes within the path (intra-path contrast), we also designed a method of contrasting the relationship between the paths (inter-path contrast) to better pull the target node and its path nodes closer to each other. We use Simplifying and Powering Graph Convolution Network (LightGCN) as the basis and combine with a new path-enhanced graph approach proposed for graph augmentation. It effectively improves the performance of recommendation models. Our proposed Path Enhanced Contrastive Loss (PECL) model replaces the common contrastive loss function with our novel loss function, showing significant performance improvement. Experiments on three real-world datasets demonstrate the effectiveness of our model.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a path-enhanced contrastive learning for graph-based recommendation. Specifically, the authors consider graph-related augmentations based on both intra-path and inter-path correlations for better representation learning. In experiments, the authors have conducted evaluations on two datasets and achieved better results compared the baselines. They also verify the effectiveness of their two CL strategies in ablation study.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n\n1) The idea is sound and the writing is clear.\n2) The authors have conducted ablation study and parameter analysis to better understanding the effects of different components and parameters in their model.\n\n### Weaknesses:\n\n1) The major weakness of this work is the lack of sufficient related work that also concentrate on graph contrastive learning in recommendation. There are lots of GCL related works for recommendation and other fields [1-5], some of which also consider path-related information to build positive/negative samples in CL. The authors should (i) appropriately cite and introduce these related efforts, giving the detailed pros and cons of their proposed inter-/intra- CL compared to existing methods, and (ii) compare with recent good-performing GCL methods (at least better than the classical backbone lightGCN on the evaluation datasets to ensure the persuasiveness of the conclusion).\n2) Are the current baselines strong enough and were they properly trained? It is noticed that most of the compared baselines are proposed 2 years ago. The performance of SCCF (most recent baseline) is relatively poor (basically inferior to simGCL and even lightGCN), while simGCL and lightGCN are also comparable. The authors need to explain these unsatisfactory results of GCL baselines, and it is also recommended that the author release the codes and specific settings of essential baselines for reproducibility.\n3) Whether the performance differences mainly derive from the amount of augmentation samples rather than the specific augmentation methods? The authors could show the number of augmentation samples of different methods, as well as their real training costs.\n4) The current evaluations are mainly based on two datasets, and more datasets are required.\n5) Whether PECL w/o intra-path and inter-path CL equals LightGCN? If so, it seems that both intra-path and inter-path CL strategies have nearly 4-6% improvement in NDCG@10 in ML-1M, while other baselines’ CL strategies have far less advantages. More discussions are needed.\n\n\n### References:\n\n[1] Xu L, Liu Y, Xu T, et al. Graph augmentation empowered contrastive learning for recommendation[J]. ACM Transactions on Information Systems, 2025, 43(2): 1-27.\n\n[2] Huang J, Cao Q, Xie R, et al. Adversarial learning data augmentation for graph contrastive learning in recommendation[C]//International Conference on Database Systems for Advanced Applications. Cham: Springer Nature Switzerland, 2023: 373-388.\n\n[3] Zhang S, Chen L, Wang C, et al. Temporal graph contrastive learning for sequential recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(8): 9359-9367.\n\n[4] Zhang W, Yang L, Song Z, et al. Mixed Supervised Graph Contrastive Learning for Recommendation[J]. arXiv preprint arXiv:2404.15954, 2024.\n\n[5] Sang L, Wang Y, Zhang Y, et al. Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2025.\n\nQUESTIONS:\nRefer to Weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces PECL, a contrastive learning framework for collaborative filtering recommendations. Instead of relying solely on conventional node-level graph augmentations, PECL proposes to use paths—specifically center and positive paths—as the basis for contrastive supervision. Two loss components are proposed: intra-path contrast, which encourages alignment between a target node and its intra-path neighbors, and inter-path contrast, which promotes consistency across semantically similar paths. To facilitate this, a time-aware path sampling strategy is developed. The model achieves strong improvements over state-of-the-art baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. This paper offers a compelling advancement in contrastive learning for recommendation. By using interaction paths instead of individual nodes as contrastive units, the model aligns more closely with the actual semantics of user-item interactions. This represents a meaningful shift in perspective and offers a new direction for future work in this area.\n2. The time-aware sampling mechanism is particularly thoughtful, aligning path selection with real-world behavioral patterns. This adds interpretability and practical relevance to the model, making it not only more effective but also more aligned with real-world data dynamics.\n3. The experimental results are robust and well-analyzed. PECL outperforms competitive baselines across all major metrics, and the performance gains on both datasets.\n4. The technical depth and clarity of the paper are its strengths. Despite the complexity of the proposed contrastive losses and sampling strategies, the methodology is described in a step-by-step, reproducible manner, supported by code, formulas, figures, and pseudocode in supplementary materials and appendix.\nWeaknesses:\n1. The experimental evaluation focuses solely on Recall and NDCG, but does not assess Hit Ratio or Precision, which are often discussed in recommender systems.\n2. Although several contrastive methods are compared, the performance gap between PECL and strong non-contrastive methods is not discussed in depth.\n3. The model incorporates timestamps in path sampling and encoding, but it does not explain why the temporal dynamics are fixed through sinusoidal functions.\n\nQUESTIONS:\n1. Could the authors report additional contrastive evaluation metrics?\n2. How does PECL compare to non-contrastive recommendation models with complex encoders?\n3. Have the authors considered learning time encodings jointly, rather than relying on fixed features?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces PECL, a novel framework that enhances collaborative filtering by explicitly modeling contrastive signals between and within sampled interaction paths. Unlike prior methods that treat contrastive learning at the node or neighbor level, PECL constructs center paths and mines time-aligned positive paths to form more semantically coherent self-supervision signals. The intra-path loss encourages local consistency between a node and its path neighbors, while the inter-path loss aligns global structure across multiple paths. The authors also introduce a two-stage path sampling mechanism guided by temporal behavior. Experimental evaluations show that PECL consistently outperforms state-of-the-art methods, supported by extensive ablation and hyperparameter analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n(1) PECL is built on LightGCN and adopts a plug-in architecture, allowing its contrastive objectives to be easily integrated into other GNN-based recommenders, improving usability and adaptability in practice.\n\n(2) The model’s use of multi-hop paths enhances its ability to represent users and items in sparse environments, making it more robust to data imbalance and cold-start scenarios.\n\n(3) Experiments show consistent improvements over baselines, especially on ML-1M. Ablation studies validate the contributions of each component, reinforcing the method’s effectiveness.\n\n(4) The core ideas—path-based contrast and time-aware sampling—are generalizable, with potential use in temporal graphs, session data, and sequential modeling beyond recommendation.\n\nWeaknesses:\n\n(1) While the method is well-motivated, its reliance on multiple hyperparameters may limit applicability. The paper could benefit from providing practical guidelines or heuristics for tuning these values in new datasets.\n\n(2) The inter-path loss introduces some additional computational complexity. Discussion or benchmarking on runtime and memory usage would strengthen the claims of scalability.\n\n(3) The gains on the Ciao dataset, though consistent, are relatively modest. It would be helpful if the authors could comment on why the performance gap is narrower compared to ML-1M (e.g., sparsity).\n\nQUESTIONS:\n(1) Could the authors provide a rough estimate of training time or memory usage compared to LightGCN or SGL?\n\n(2) Would the model still perform well with a simpler temporal encoding scheme, such as scalar time embeddings?\n\n(3) Since PECL depends on pre-sampled and encoded paths, can the model be efficiently used in an online recommendation service?\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel PECL framework for collaborative filtering recommendation. While previous contrastive learning methods focus primarily on node-level consistency using graph augmentation, this work innovatively integrates both intra-path and inter-path contrastive learning strategies to enhance the representation of users and items. Specifically, the authors design a time-aware path sampling strategy and path-level contrastive loss that bring semantically similar paths closer, thereby mitigating issues arising from graph perturbations in conventional methods. Experiments demonstrate PECL’s performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nS1: The paper presents a technically sound and well-motivated framework. The derivation of contrastive loss functions, the path encoding design using Hermitian inner products, and the integration of time-aware sampling are all carefully described. Experimental results are thorough and convincing, with statistically significant improvements over recent baselines across multiple datasets.\n\nS2: The contribution is meaningful to the recommender system and self-supervised learning communities. By explicitly modeling both intra- and inter-path relations, the paper expands the scope of contrastive learning in recommendation beyond node-level augmentation. This can inspire further research in structured or temporal-aware contrastive objectives.\n\nS3: The idea of contrasting sampled paths instead of just nodes is a notable and original extension to existing self-supervised learning methods in recommendation. PECL introduces a new level of abstraction by leveraging paths as the unit of contrast, which is both novel and effective.\n\nWeaknesses:\n\nW1: There are occasional grammatical errors and formatting inconsistencies (e.g., duplicated phrases like “effectively extract meaningful features from large-scale unlabeled data effectively”), which can be polished in a revision.\n\nW2: The use of Hermitian inner product in complex space is technically novel, but readers may find it hard to interpret how it improves path representation in practice, especially without visualizations or ablations isolating this factor.\n\nW3: The current framework seems closely tied to bipartite user-item graphs; it is unclear how flexible the method is when applied to more complex graph structures, such as heterogeneous.\n\nQUESTIONS:\nQ1: Is there a risk of overfitting if sampled paths are highly similar?\n\nQ2:  While ML-1M and Ciao are popular benchmarks, have the authors considered testing PECL on larger-scale datasets (e.g., Amazon, Yelp)? \n\nQ3: Could the authors comment on whether longer or redundant paths may sometimes harm performance?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}