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Knowledge Graph-based Recommendation
UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration
Published:10/28/2025
Generative Recommendation SystemsKnowledge Graph-based RecommendationPersonalized Recommendation SystemMultimodal Recommendation SystemsOnline Recommendation System Optimization
The paper introduces UNGER, a generative recommendation approach that integrates semantic and collaborative information into a unified code to reduce storage and inference costs. Utilizing a twophase framework for effective code construction, it demonstrates significant improvem
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Multimodal fusion framework based on knowledge graph for personalized recommendation
Published:1/1/2025
Knowledge Graph-based RecommendationMultimodal Recommendation SystemsMultimodal Fusion FrameworkPersonalized Recommendation
This work proposes MultiKG4Rec, a multimodal fusion framework leveraging finegrained modal interactions in knowledge graphs to enhance personalized recommendations, demonstrating superior efficiency on realworld datasets.
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Knowledge graph-based personalized multimodal recommendation fusion framework
Published:1/1/2025
Knowledge Graph-based RecommendationMultimodal Recommendation SystemsCross-Modal Multi-Head Cross-AttentionGraph Attention NetworksPretrained Vision-Text Models
We propose CrossGMMIDUKGLR, a knowledge graphbased multimodal recommendation framework using pretrained visualtext alignment, multihead crossattention, and graph attention networks to enhance feature fusion and capture higherorder dependencies for improved personalization.
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Knowledge Graph Convolutional Networks for Recommender Systems
Published:3/19/2019
Knowledge Graph-based RecommendationGNN-based Recommender SystemsCold-Start ProblemHigh-Order Structural Information ModelingNeighbor Sampling Mechanism
KGCN uses neighbor sampling to capture highorder structural and semantic information in knowledge graphs, addressing sparsity and coldstart in recommender systems. It performs well on largescale datasets in movie, book, and music recommendations.
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Learning Intents behind Interactions with Knowledge Graph for
Recommendation
Published:2/14/2021
Knowledge Graph-based RecommendationGNN-based Recommendation ModelsUser Intent ModelingLong-Range Dependency ModelingIntent-Driven Relation Aggregation
KGIN models finegrained user intents via attentive relation combinations and recursive relation path aggregation, improving longrange dependency modeling and outperforming existing GNNbased recommenders on benchmarks.
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