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Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-59419-0_33openalex-snapshot
Mutual Self Attention Recommendation with Gated Fusion Between Ratings and Reviews
inferredcites →https://doi.org/10.1007/978-3-030-73200-4_6openalex-snapshot
Semi-supervised Factorization Machines for Review-Aware Recommendation
inferredcites →https://doi.org/10.1007/978-3-319-93417-4_38openalex-snapshot
Modeling Relational Data with Graph Convolutional Networks
inferredcites →https://doi.org/10.1007/978-3-319-98539-8_6openalex-snapshot
E-Commerce Product Recommendation Using Historical Purchases and Clickstream Data
inferredcites →https://doi.org/10.1016/j.neucom.2019.09.052openalex-snapshot
Hybrid neural recommendation with joint deep representation learning of ratings and reviews
inferredcites →https://doi.org/10.1038/44565openalex-snapshot
Learning the parts of objects by non-negative matrix factorization
inferredcites →https://doi.org/10.1109/tkde.2018.2831682openalex-snapshot
NAIS: Neural Attentive Item Similarity Model for Recommendation
inferredcites →https://doi.org/10.1145/2507157.2507163openalex-snapshot
Hidden factors and hidden topics
inferredcites →https://doi.org/10.1145/2623330.2623758openalex-snapshot
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)
inferredcites →https://doi.org/10.1145/3018661.3018665openalex-snapshot
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
inferredcites →https://doi.org/10.1145/3109859.3109890openalex-snapshot
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction
inferredcites →https://doi.org/10.1145/3178876.3186070openalex-snapshot
Neural Attentional Rating Regression with Review-level Explanations
inferredcites →https://doi.org/10.1145/3219819.3219947openalex-snapshot
Large-Scale Learnable Graph Convolutional Networks
inferredcites →https://doi.org/10.1145/3219819.3220086openalex-snapshot
Multi-Pointer Co-Attention Networks for Recommendation
inferredcites →https://doi.org/10.1145/3292500.3330673openalex-snapshot
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
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/10.1145/3292500.3330961openalex-snapshot
Heterogeneous Graph Neural Network
inferredcites →https://doi.org/10.1145/3308558.3313562openalex-snapshot
Heterogeneous Graph Attention Network
inferredcites →https://doi.org/10.1145/3309546openalex-snapshot
Attentive Aspect Modeling for Review-Aware Recommendation
inferredcites →https://doi.org/10.1145/3331184.3331267openalex-snapshot
Neural Graph Collaborative Filtering
inferredcites →https://doi.org/10.1145/3340531.3411939openalex-snapshot
Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation
inferredcites →https://doi.org/10.1145/3357384.3357919openalex-snapshot
Leveraging Ratings and Reviews with Gating Mechanism for Recommendation
inferredcites →https://doi.org/10.1145/3366423.3380027openalex-snapshot
Heterogeneous Graph Transformer
inferredcites →https://doi.org/10.1145/3366423.3380297openalex-snapshot
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
inferredcites →https://doi.org/10.1145/3397271.3401252openalex-snapshot
A Heterogeneous Graph Neural Model for Cold-start Recommendation
inferredcites →https://doi.org/10.1145/3442381openalex-snapshot
Proceedings of the Web Conference 2021
inferredcites →https://doi.org/10.1145/3535101openalex-snapshot
Graph Neural Networks in Recommender Systems: A Survey
inferredcites →https://doi.org/10.1145/3548455openalex-snapshot
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
inferredcites →https://doi.org/10.1145/963770.963776openalex-snapshot
Item-based top- N recommendation algorithms
inferredcites →https://doi.org/10.1609/aaai.v33i01.33014602openalex-snapshot
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v34i01.5330openalex-snapshot
Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
inferredcites →https://doi.org/10.1609/aaai.v35i12.17315openalex-snapshot
Data Augmentation for Graph Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v35i5.16533openalex-snapshot
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
inferredcites →https://doi.org/10.2200/s00433ed1v01y201207dmk005openalex-snapshot
Mining Heterogeneous Information Networks: Principles and Methodologies
inferredcites →https://doi.org/10.3390/fi11010005openalex-snapshot
Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1706.02263openalex-snapshot
Graph Convolutional Matrix Completion
inferredcites →https://doi.org/10.48550/arxiv.2002.02126openalex-snapshot
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10115-024-02309-xopenalex-snapshot
Enhanced multimodal recommendation systems through reviews integration
inferred← citeshttps://doi.org/10.1016/j.datak.2025.102417openalex-snapshot
Aspect-level recommendation fused with review and rating representations
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.107499openalex-snapshot
Keyword-enhanced recommender system based on inductive graph matrix completion
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112196openalex-snapshot
Semantic analysis-based recommender system using sequential clustering and convolutional neural network
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128990openalex-snapshot
Exploiting attributes and keywords for session-based recommendation with multi-view graph neural network
inferred← citeshttps://doi.org/10.1016/j.ins.2024.120655openalex-snapshot
Aspect-level item recommendation based on user reviews with variational autoencoders
inferred← citeshttps://doi.org/10.1109/icws60048.2023.00057openalex-snapshot
Context-aware Inductive Graph Matrix Completion with Sentence BERT
inferred← citeshttps://doi.org/10.1145/3609334openalex-snapshot
Discrete Listwise Content-aware Recommendation
inferred← citeshttps://doi.org/10.1145/3611310openalex-snapshot
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
inferred← citeshttps://doi.org/10.1145/3615896.3628344openalex-snapshot
Rating Inference for Custom Trips from Enriched GPS Traces using Random Forests
inferred← citeshttps://doi.org/10.1145/3618106openalex-snapshot
Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation
inferred← citeshttps://doi.org/10.1145/3744661openalex-snapshot
Review-Aware Recommender Systems (RARSs): Recent Advances, Experimental Comparative Analysis, Discussions, and New Directions
inferred← citeshttps://doi.org/10.1177/01655515261474838openalex-snapshot
Personalized wine recommendations in e-commerce: Integrating knowledge graphs and graph neural networks
inferred← citeshttps://doi.org/10.12794/metadc2048609openalex-snapshot
Personalized Recommendation Using Aspect-Aware Knowledge Graph Learning
inferred← citeshttps://doi.org/10.1609/aaai.v38i8.28761openalex-snapshot
Review-Enhanced Hierarchical Contrastive Learning for Recommendation
inferred← citeshttps://doi.org/10.2139/ssrn.6502552openalex-snapshot
DGCL-Fusion: Dual-Graph Contrastive Learning with Global Fusion Attention for Higher-Order Information Enhanced Sparse Recommendations
inferred← citeshttps://doi.org/10.5753/webmedia.2025.14831openalex-snapshot
Uma Abordagem Híbrida para Sistemas de Recomendação com Base em Avaliações Textuais e Filtragem Colaborativa
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