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DGCL-Fusion: Dual-Graph Contrastive Learning with Global Fusion Attention for Higher-Order Information Enhanced Sparse Recommendations

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/0306-4573(89)90048-4openalex-snapshot

Similarity measures in scientometric research: The Jaccard index versus Salton's cosine formula

inferredcites →https://doi.org/10.1016/j.is.2023.102334openalex-snapshot

Attention-based multi attribute matrix factorization for enhanced recommendation performance

inferredcites →https://doi.org/10.1108/dta-04-2022-0172openalex-snapshot

Impact on recommendation performance of online review helpfulness and consistency

inferredcites →https://doi.org/10.1109/2.36openalex-snapshot

Self-organization in a perceptual network

inferredcites →https://doi.org/10.1109/icpads60453.2023.00215openalex-snapshot

Rating-Review Graph Contrastive Learning for Review-based Recommendation

inferredcites →https://doi.org/10.1145/2623330.2623758openalex-snapshot

Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)

inferredcites →https://doi.org/10.1145/2783258.2783273openalex-snapshot

Collaborative Deep Learning for Recommender Systems

inferredcites →https://doi.org/10.1145/3018661.3018665openalex-snapshot

Joint Deep Modeling of Users and Items Using Reviews for Recommendation

inferredcites →https://doi.org/10.1145/3109859.3109878openalex-snapshot

TransNets

inferredcites →https://doi.org/10.1145/3178876.3186070openalex-snapshot

Neural Attentional Rating Regression with Review-level Explanations

inferredcites →https://doi.org/10.1145/3292500.3330906openalex-snapshot

DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation

inferredcites →https://doi.org/10.1145/3298988openalex-snapshot

A Context-Aware User-Item Representation Learning for Item Recommendation

inferredcites →https://doi.org/10.1145/3485447.3512104openalex-snapshot

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

inferredcites →https://doi.org/10.1145/3532188openalex-snapshot

Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation

inferredcites →https://doi.org/10.1145/3618106openalex-snapshot

Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation

inferredcites →https://doi.org/10.1609/aaai.v34i05.6268openalex-snapshot

Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.