Here we provide the list of articles, which were used for reproducibility study

SN Venue Title Repository Techniques
1 SIGIR 2020 Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates Yes Multi-channel and Attention mechanism
2 SIGIR 2020 Disentangled Graph Collaborative Filtering Yes Graph neural networks
3 SIGIR 2023 Disentangled Contrastive Collaborative Filtering Yes Graph neural networks and Graph contrastive learning
4 SIGIR 2024 Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering Yes Graph convolution networks and Graph contrastive learning
5 ACM Transactions on Information Systems 2023 (IF:5.4) Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement No Knowledge graphs and Relation aggregation
6 Applied Intelligence 2023 (IF:5.3) Entity-driven user intent inference for knowledge graph-based recommendation No Knowledge Graphs
7 Applied Intelligence 2023 (IF:5.3) FIRE: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks No Graph neural networks
8 ACL 2020 Graph Neural News Recommendation with Unsupervised Preference Disentanglement No Graph neural networks
9 IEEE Transactions on Knowledge and Data Engineering 2022 (IF: 11.7) Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation No Meta-Paths, Causal intervention and Graph neural networks
10 IEEE Transactions on Knowledge and Data Engineering 2022 (IF: 11.7) Intent Disentanglement and Feature Self-supervision for Novel Recommendation Yes Special disentangling approach
11 IEEE Transactions on Multimedia 2022 (IF: 8.2) Hierarchical User Intent Graph Network for Multimedia Recommendation Yes Graph neural networks and Graph contrastive learning
12 ICDE 2022 Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation No Disentangling, Clustering and Contrastive learning
13 WWW 2021 Learning Intents behind Interactions with Knowledge Graph for Recommendation Yes Graph neural networks and Knowledge graphs