Causal Inspired Multi Modal Recommendation
Multimodal recommender systems enhance personalized recommendations in e-commerce and online advertising by integrating visual, textual, and user-item interaction data. However, existing methods often overlook two critical biases: (i) modal confounding, where latent factors (e.g., brand style or product category) simultaneously drive multiple modalities and influence user preference, leading to spurious feature-preference associations; (ii) interaction bias, where genuine user preferences are mixed with noise from exposure effects and accidental clicks. To address these challenges, we propose a Causal-inspired multimodal Recommendation framework. Specifically, we introduce a dual-channel cross-modal diffusion module to identify hidden modal confounders, utilize back-door adjustment with hierarchical matching and vector-quantized codebooks to block confounding paths, and apply front-door adjustment combined with causal topology reconstruction to build a deconfounded causal subgraph. Extensive experiments on three real-world e-commerce datasets demonstrate that our method significantly outperforms state-of-the-art baselines while maintaining strong interpretability.
Code (0)
등록된 구현이 없습니다.
Tasks
Multimodal RecommendationSimilar Papers 제목 키워드 기반
Causality-Inspired Fair Representation Learning for Multimodal Recommendation
Recently, multimodal recommendations (MMR) have gained increasing attention for alleviating the data sparsity problem of traditional recommender systems by incorporating modality-based representations. Although MMR exhib…
AttributeCausal InferencecounterfactualFairness+4Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations
Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recently, a few efforts have sought to improve …
counterfactualFairnessMultimodal RecommendationLLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
Contemporary generative recommendation systems face significant challenges in handling multimodal data, eliminating algorithmic biases, and providing transparent decision-making processes. This paper introduces an enhanc…
Computational EfficiencyRecommendation SystemsCausal InferenceDynamic Causal Collaborative Filtering
Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of rec…
Collaborative FilteringcounterfactualCounterfactual ReasoningRecommendation SystemsAn adaptive denoising recommendation algorithm for causal separation bias
In recommender systems, user selection bias often influences user-item interactions, e.g., users are more likely to rate their previously preferred or popular items. Existing methods can leverage the impact of selection …
Causal InferenceDenoisingMulti-Task LearningRecommendation Systems+1