Multimodal Learning toward Recommendation
Multimodal Learning toward Recommendation
English | 2025 | ISBN: 303183187X | 163 Pages | PDF (True) | 13 MB
English | 2025 | ISBN: 303183187X | 163 Pages | PDF (True) | 13 MB
First, it presents a semantic-guided feature distillation method which employs a teacher-student framework to robustly extract effective recommendation-oriented features from generic multimodal features. Next, it introduces a novel multimodal attentive metric learning method to model user diverse preferences for various items. Then it proposes a disentangled multimodal representation learning recommendation model, which can capture users’ fine-grained attention to different modalities on each factor in user preference modeling. Furthermore, a meta-learning-based multimodal fusion framework is developed to model the various relationships among multimodal information. Building on the success of disentangled representation learning, it further proposes an attribute-driven disentangled representation learning method, which uses attributes to guide the disentanglement process in order to improve the interpretability and controllability of conventional recommendation methods. Finally, the book concludes with future research directions in multimodal learning toward recommendation.