Knowledge-Aware Recommendation
7 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Ekar: An Explainable Method for Knowledge Aware Recommendation
DeepGraphLearning/RecommenderSystems
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Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations.
KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems
RUCDM/KB4Rec
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Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i. e. popularity and recency) on whether a RS item can be linked to a KB entity.
HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
zealscott/hakg
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Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns.
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
cciiplab/mcclk
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Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views.
Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
cciiplab/kgic
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Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR.
Knowledge-refined Denoising Network for Robust Recommendation
xj-zhu98/krdn
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Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability.
Knowledge-aware Dual-side Attribute-enhanced Recommendation
tjtp/kdar
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Specifically, we build \textit{user preference representations} and \textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \textit{collaborative filtering} (CF) based user and item representations, respectively.
