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EliMRec: Eliminating Single-modal Bias in Multimedia Recommendation

Xiaohao Liu, Zhulin Tao, Jiahong Shao, Lifang Yang, Xianglin Huang

2022Proceedings of the 30th ACM International Conference on Multimedia19 citationsDOI

Abstract

The main idea of multimedia recommendation is to introduce the profile content of multimedia documents as an auxiliary, so as to endow recommenders with generalization ability and gain better performance. However, recent studies using non-uniform datasets roughly fuse single-modal features into multi-modal features and adopt the strategy of directly maximizing the likelihood of user preference scores, leading to the single-modal bias. Owing to the defect in architecture, there is still room for improvement for recent multimedia recommendation.

Topics & Concepts

ModalComputer scienceFuse (electrical)GeneralizationMultimediaRecommender systemPreferenceArchitectureInformation retrievalArtificial intelligenceEngineeringStatisticsMathematicsElectrical engineeringChemistryVisual artsMathematical analysisArtPolymer chemistryRecommender Systems and TechniquesImage Retrieval and Classification TechniquesAdvanced Image and Video Retrieval Techniques
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