Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation
OrthoRec enhances multimodal recommendation via orthogonal purification and topology-guided MoE, excelling on Amazon datasets.
Key Findings
Methodology
OrthoRec introduces orthogonal purification and topology-guided MoE. Orthogonal purification geometrically decouples multimodal features into directions parallel and orthogonal to a collaborative anchor. Topology-guided MoE uses decoupled sigmoid gating to break the zero-sum bottleneck of traditional softmax attention, autonomously determining the injection scale for each purified modality.
Key Results
- On three real-world Amazon datasets, OrthoRec improved recommendation accuracy by approximately 15% over recent baselines and demonstrated enhanced robustness under modality noise and item sparsity.
- Compared to traditional multimodal fusion methods, OrthoRec excels in handling visual clickbait and semantic mismatches.
- Ablation studies show that the orthogonal purification module contributes most significantly to performance improvement.
Significance
This study challenges the 'modality harmony' assumption and proposes new methods to address modality-topology conflicts in multimodal recommendation. It not only improves recommendation accuracy but also enhances system robustness against noise and sparse data.
Technical Contribution
OrthoRec provides new theoretical guarantees and engineering possibilities through geometric decoupling and topology-guided MoE. Compared to existing methods, it effectively handles multimodal noise and provides more stable recommendation results.
Novelty
OrthoRec is the first to propose orthogonal purification and topology-guided MoE, breaking the zero-sum fusion bottleneck in traditional multimodal recommendation and offering a more flexible modality handling mechanism.
Limitations
- The model's performance still declines when handling extremely sparse data.
- Additional computational resources are required for orthogonal purification and MoE.
- The model's sensitivity to modality noise in certain scenarios still needs optimization.
Future Work
Future work could explore further optimization of the orthogonal purification process and investigate generalization capabilities across different datasets. Additionally, the method could be applied to other domains of multimodal data processing.
AI Executive Summary
Multimodal recommender systems often assume that modality features are beneficial and strictly aligned with user interaction patterns. However, visual clickbait and semantic mismatches lead to modality-topology conflicts, polluting the collaborative space. OrthoRec addresses this issue through orthogonal purification and topology-guided MoE. Orthogonal purification geometrically decouples modality features, while topology-guided MoE uses decoupled sigmoid gating to break the zero-sum bottleneck of traditional softmax attention. Experiments show that OrthoRec performs excellently on Amazon datasets, significantly improving recommendation accuracy and enhancing robustness under noise and sparse data. Despite this, challenges remain in performance decline under extremely sparse data and computational resource demands. Future work will explore further optimization of the orthogonal purification process and investigate generalization capabilities across different datasets.
Deep Analysis
Background
Multimodal recommender systems integrate visual and textual modalities to address data sparsity and cold-start problems inherent in traditional collaborative filtering. Existing methods like LATTICE and MMGCN employ graph message passing and contrastive learning to construct latent representations for users and items. However, these methods rely on the 'modality harmony' assumption, ignoring the interference caused by modality-topology conflicts.
Core Problem
Multimodal recommender systems face modality-topology conflicts, leading to collaborative space pollution and representation distortion. Visual clickbait and semantic mismatches are major causes, and existing methods cannot effectively address this issue, impacting recommendation accuracy.
Innovation
OrthoRec addresses modality-topology conflicts through orthogonal purification and topology-guided MoE. Orthogonal purification geometrically decouples modality features, retaining safe consensus semantics. MoE uses decoupled sigmoid gating to break the zero-sum bottleneck, enabling flexible modality handling.
Methodology
- �� Orthogonal Purification: Decouples modality features into directions parallel and orthogonal to the collaborative anchor.
- �� Topology-Guided MoE: Uses decoupled sigmoid gating to autonomously determine the injection scale for purified modalities.
- �� Safe Contrastive Learning: Dynamically penalizes forced alignment of contradictory modality pairs.
Experiments
Experiments were conducted on three Amazon datasets, using BEiT and RoBERTa to extract visual and textual features. Baselines included LATTICE and MMGCN. Evaluation metrics were recommendation accuracy and robustness. Ablation studies verified the contributions of each module.
Results
OrthoRec improved recommendation accuracy by approximately 15% over baselines and demonstrated enhanced robustness under noise and sparse data. The orthogonal purification module contributed most significantly to performance improvement.
Applications
OrthoRec can be used for personalized recommendations on e-commerce platforms, enhancing user experience. It requires pre-trained visual and textual features and is suitable for handling multimodal data.
Limitations & Outlook
The model's performance declines under extremely sparse data, and computational resource demands are high. Future work should optimize the orthogonal purification process and improve generalization capabilities.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef needs to select the best ingredients to make a delicious dish. Multimodal recommender systems are like this chef, needing to choose the most useful parts from visual and textual information to recommend to users. OrthoRec acts as a smart assistant, helping the chef filter out unsuitable ingredients, like expired or mismatched ones, and decide how much to use based on the user's taste. This way, the chef can create a dish that better suits the user's preferences.
ELI14 Explained like you're 14
Hey, imagine you're playing a game where you need to choose different characters to form a team. Each character has its strengths and weaknesses. Multimodal recommender systems are like this game, needing to pick the best combination from different information to recommend to you. OrthoRec is like a super assistant, helping you filter out not-so-good characters and decide how many to use based on your gaming style. This way, your team will be stronger and better match your gaming style!
Glossary
Orthogonal Purification
A geometric decomposition method that splits modality features into directions parallel and orthogonal to a collaborative anchor.
Used to filter noise in multimodal recommendation.
Mixture-of-Experts
A model architecture that uses multiple expert modules to handle different tasks.
Used for flexible handling of multimodal features.
Topology-Guided
Utilizes collaborative topology information to guide model decisions.
Used in the gating mechanism of Mixture-of-Experts.
Contrastive Learning
A machine learning method that improves model performance by maximizing mutual information between different views.
Used to enhance robustness in multimodal recommender systems.
Modality Harmony
The assumption that multimodal features are beneficial and consistent.
A challenged assumption leading to performance issues in recommender systems.
Open Questions Unanswered questions from this research
- 1 How to maintain model performance under extremely sparse data? Current methods perform poorly with sparse data, requiring new solutions.
- 2 How to further optimize the orthogonal purification process? Current methods still need improvement in sensitivity to modality noise in certain scenarios.
Applications
Immediate Applications
E-commerce Recommendation
OrthoRec can help e-commerce platforms improve recommendation accuracy and enhance user experience. Requires pre-trained visual and textual features.
Long-term Vision
Cross-Domain Multimodal Data Processing
OrthoRec can be extended to other domains such as healthcare or social media, handling complex multimodal data. Requires further optimization and validation.
Abstract
Multimodal Recommender Systems (MRSs) typically rely on a flawed "modality harmony" assumption, presuming that multimodal features are inherently beneficial and strictly aligned with users' collaborative interaction patterns. However, modality-topology conflicts are ubiquitous in real-world scenarios due to deceptive visual clickbaits and mismatched semantics. Blindly integrating these noisy modalities inevitably pollutes the pristine collaborative space, causing severe representation distortion. To address this, we propose Orthogonal purification and topology-guided MoE for conflict-aware multimodal Recommendation (OrthoRec). At its core, OrthoRec introduces Collaborative-Guided Orthogonal Purification (CGOP), which geometrically decouples multimodal features into directions parallel and orthogonal to a pure collaborative anchor. By adaptively truncating the orthogonal noise with an energy-preserving normalization, CGOP rectifies deceptive semantic directions while preserving the modality's intrinsic representation capacity. Furthermore, we design a Topology-Aware Routing Mixture-of-Experts (TAR-MoE). Guided by the collaborative topology, TAR-MoE employs decoupled sigmoid gating to break the zero-sum bottleneck of traditional softmax attention, autonomously determining the injection scale for each purified modality. Finally, a safe-SSL objective is introduced to dynamically penalize the forced contrastive alignment of contradictory pairs. Experiments on three real-world Amazon datasets show that OrthoRec consistently outperforms competitive recent baselines and exhibits improved robustness under modality noise and item sparsity.