SymCERE: Symmetric Contrastive Learning for Robust Review-Enhanced Recommendation

• URL: http://arxiv.org/abs/2504.02195v2
• Date: Wed, 13 Aug 2025 10:03:30 GMT
• Title: SymCERE: Symmetric Contrastive Learning for Robust Review-Enhanced Recommendation
• Authors: Toyotaro Suzumura, Hisashi Ikari, Hiroki Kanezashi, Md Mostafizur Rahman, Yu Hirate,
• Abstract summary: We propose SymCERE, a contrastive learning method that addresses false negatives in recommendation.<n>Experiments on 15 datasets from three platforms demonstrate that SymCERE outperforms several strong baselines.
• Score: 2.087411180679868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern recommendation systems can achieve high performance by fusing user behavior graphs (via GNNs) and review texts (via LLMs). However, this fusion faces three significant issues: (1) False Negatives in contrastive learning can degrade the training signal by penalizing similar items; (2) Popularity Bias, often encoded as embedding magnitude, can distort similarity scores; and (3) Signal Ambiguity, which arises from the conflation of objective facts with subjective sentiment in reviews. These interconnected issues can prevent models from learning users' true preferences. In this paper, we propose SymCERE (Symmetric SINCERE), a contrastive learning method that addresses these three issues simultaneously through its structural design. First, we introduce a symmetric application of the SINCERE loss for cross-modal alignment, which is designed to eliminate false negatives in recommendation. Second, by integrating this with L2 normalisation under a "magnitude-as-noise" hypothesis, we aim to mitigate popularity bias by forcing the model to encode preferences primarily in the vector's direction. Experiments on 15 datasets from three distinct platforms (e-commerce, local reviews, and travel) demonstrate that SymCERE outperforms several strong baselines, achieving a relative improvement of up to 43.6% on NDCG@10. Furthermore, a detailed LIME analysis shows that the model learns to anchor alignment on objective, informative vocabulary (e.g., "OEM," "compatible," "gasket"), while placing less emphasis on generic sentiment (e.g., "good," "great"). This suggests that effective semantic alignment stems from understanding factual product attributes, offering a path toward more accurate recommendation systems. The code is available at: https://anonymous.4open.science/r/ReviewGNN-2E1E.

Related papers

• End-to-End Personalization: Unifying Recommender Systems with Large Language Models [0.0]
  We propose a novel hybrid recommendation framework that combines Graph Attention Networks (GATs) with Large Language Models (LLMs)<n>LLMs are first used to enrich user and item representations by generating semantically meaningful profiles based on metadata such as titles, genres, and overviews.<n>We evaluate our model on benchmark datasets, including MovieLens 100k and 1M, where it consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-08-02T22:46:50Z)
• Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering [16.608428889271202]
  Current metric learning methods aim to push negative items away from the corresponding users and positive items by an absolute geometrical distance margin.<n>We propose a new method, named discrete scale-invariant metric learning (DSIML), by adding binary constraints to users and items.<n> Experiments on benchmark datasets clearly show that our proposed method is superior to competitive metric learning and hashing-based baselines for recommender systems.
  arXiv  Detail & Related papers  (2025-06-11T16:13:52Z)
• Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting [86.15347226865826]
  We design a new end-to-end object-aware lifting approach, named Unified-Lift.<n>We augment each Gaussian point with an additional Gaussian-level feature learned using a contrastive loss to encode instance information.<n>We conduct experiments on three benchmarks: LERF-Masked, Replica, and Messy Rooms.
  arXiv  Detail & Related papers  (2025-03-18T08:42:23Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• RecLM: Recommendation Instruction Tuning [17.780484832381994]
  We propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering. Our proposed $underlineRec$ommendation enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function.
  arXiv  Detail & Related papers  (2024-12-26T17:51:54Z)
• Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning [10.907949155931474]
  Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Gal-Rec enhances the understanding of user-item collaborative semantics by imitating the intent of Graph Neural Networks (GNNs) Gal-Rec significantly enhances the comprehension of collaborative semantics, and improves recommendation performance.
  arXiv  Detail & Related papers  (2024-06-19T05:50:15Z)
• Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation [13.300975621769396]
  We propose a novel textbfLight textbfSigned textbfGraph Convolution Network specifically for textbfRecommendation (textbfLSGRec) For the negative preferences within high-order heterogeneous interactions, first-order negative preferences are captured by the negative links. recommendation results are generated based on positive preferences and optimized with negative ones.
  arXiv  Detail & Related papers  (2024-03-13T05:00:42Z)
• Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation [6.790779112538357]
  We present underlinePrototypical contrastive learning through underlineAlignment and underlineUniformity for recommendation. Specifically, we first propose prototypes as a latent space to ensure consistency across different augmentations from the origin graph. The absence of explicit negatives means that directly optimizing the consistency loss between instance and prototype could easily result in dimensional collapse issues.
  arXiv  Detail & Related papers  (2024-02-03T08:19:26Z)
• RecExplainer: Aligning Large Language Models for Explaining Recommendation Models [50.74181089742969]
  Large language models (LLMs) have demonstrated remarkable intelligence in understanding, reasoning, and instruction following. This paper presents the initial exploration of using LLMs as surrogate models to explain black-box recommender models. To facilitate an effective alignment, we introduce three methods: behavior alignment, intention alignment, and hybrid alignment.
  arXiv  Detail & Related papers  (2023-11-18T03:05:43Z)
• Representation Learning with Large Language Models for Recommendation [33.040389989173825]
  We propose a model-agnostic framework RLMRec to enhance recommenders with large language models (LLMs)empowered representation learning.<n>RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals.
  arXiv  Detail & Related papers  (2023-10-24T15:51:13Z)
• Efficient Link Prediction via GNN Layers Induced by Negative Sampling [86.87385758192566]
  Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories.<n>We propose a novel GNN architecture whereby the emphforward pass explicitly depends on emphboth positive (as is typical) and negative (unique to our approach) edges.<n>This is achieved by recasting the embeddings themselves as minimizers of a forward-pass-specific energy function that favors separation of positive and negative samples.
  arXiv  Detail & Related papers  (2023-10-14T07:02:54Z)
• TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation [1.5836776102398225]
  Long-tailed distribution of entities of KG and noise issues in the real world make item-entity dependent relations deviate from reflecting true characteristics. We design the Two-Level Debiased Contrastive Learning (TDCL) and deploy it in the knowledge graph. Considerable experiments on open-source datasets demonstrate that our method has excellent anti-noise capability.
  arXiv  Detail & Related papers  (2023-10-01T03:56:38Z)
• Graph Neural Bandits [49.85090929163639]
  We propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs) To refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration.
  arXiv  Detail & Related papers  (2023-08-21T15:57:57Z)
• Noisy-Correspondence Learning for Text-to-Image Person Re-identification [50.07634676709067]
  We propose a novel Robust Dual Embedding method (RDE) to learn robust visual-semantic associations even with noisy correspondences. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on three datasets.
  arXiv  Detail & Related papers  (2023-08-19T05:34:13Z)
• GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation [58.6450834556133]
  We propose graph contrastive learning to enhance item representations with complex associations from the global view. We extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users' dynamic preferences. Our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy.
  arXiv  Detail & Related papers  (2023-03-01T05:46:36Z)
• Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation [59.500347564280204]
  We propose a new Aleatoric Uncertainty-aware Recommendation (AUR) framework. AUR consists of a new uncertainty estimator along with a normal recommender model. As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty.
  arXiv  Detail & Related papers  (2022-09-22T04:32:51Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• Self-Supervised Hypergraph Transformer for Recommender Systems [25.07482350586435]
  Self-Supervised Hypergraph Transformer (SHT) Self-Supervised Hypergraph Transformer (SHT) Cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph.
  arXiv  Detail & Related papers  (2022-07-28T18:40:30Z)
• Debiasing Learning for Membership Inference Attacks Against Recommender Systems [79.48353547307887]
  Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. We propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components.
  arXiv  Detail & Related papers  (2022-06-24T17:57:34Z)
• Improving Contrastive Learning with Model Augmentation [123.05700988581806]
  The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity and noise issues in sequences, a new self-supervised learning (SSL) paradigm is proposed to improve the performance.
  arXiv  Detail & Related papers  (2022-03-25T06:12:58Z)
• SiReN: Sign-Aware Recommendation Using Graph Neural Networks [6.739000442575012]
  We present SiReN, a new sign-aware recommender system based on GNN models. SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.
  arXiv  Detail & Related papers  (2021-08-19T15:07:06Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.