Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks

By Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, Dik Lun Lee. 2017

562 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Recommender Systems, Knowledge Graphs & Representation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Xing Xie
5. Meng Wang
6. Chuan Shi
7. Philip S. Yu
8. Le Wu

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Meng Wang
5. Chao Huang
6. Le Wu
7. Lianghao Xia
8. Xing Xie

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Xing Xie
5. Meng Wang
6. Le Wu
7. Chao Huang
8. Lianghao Xia

In Recommender Systems

1. Xiangnan He
2. Xiang Wang
3. Xing Xie
4. Xiaohan Zou
5. Tat-seng Chua
6. Wenjie Li
7. Hongwei Wang
8. Miao Zhao

In Knowledge Graphs & Representation

1. Xiaohan Zou
2. Xiangnan He
3. Xiang Wang
4. Xing Xie
5. Miao Zhao
6. Wenjie Li
7. Hongwei Wang
8. Fuzheng Zhang

Citations in the corpus

• A Survey on Application of Knowledge Graph (2020, background): “KG is considered as a heterogeneous information-based network in the research work [27].”
• Heterogeneous Information Network Embedding for Recommendation (2017, methodology): “[38] propose a factorization machine based model integrated with meta-graph based similarity for recommendation.”
• Knowledge Graph Contrastive Learning for Recommendation (2022, methodology): “To improve KGenhanced recommender systems in capturing high-order KG connectivity, some path-based models [35, 41, 52] aim to construct pathguided user-item connections with the incorporated KG entities.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “FMG [176], MCRec [177], and KPRN [168] convert the path set into an embedding vector to represent the user-item connectivity.”
• KGAT: Knowledge Graph Attention Network for Recommendation (2019, background): “res the model to carefully weight (or select) them. Several recent efforts have attempted to leverage the CKG structure for recommendation, which can be roughly categorized into two types, path-based [14, 25, 29, 33, 37, 39] and regularizationbased [5, 15, 33, 38]: •Path-based methods extract paths ”
• Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation (2019, methodology): “Although, some HIN based recommendation methods have been proposed [8, 19, 23], they mainly employ metapath based features through exploiting the interaction relations between users and items, which makes them hardly interactions in intent recommendation. queries with heterogeneous Graph Neural…”
• Propagation Model Search for Graph Neural Networks (2020, methodology): “Our framework differs from GTN in that we search for more complex propagation patterns, i.e., meta-graphs (Zhao et al. 2017) that flexibly combine multiple meta-paths.”
• Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (2018, methodology): “[30] proposed to apply factorizationmachine to the embeddings for recommendation.”
• A Survey on Knowledge Graph-Based Recommender Systems (2020, methodology): “Traditional path-based methods [3], [75], [76], [77], [78], [79], [82] generally integrate MF with extracted meta-paths in HINs.”
• Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (2019, methodology): “…into three categories: (1) Embedding-based methods [9, 26, 27, 34] pre-process a KG with knowledge graph embedding (KGE) TransE [2] assumes head + relation = tail ), which are more suitable (2) Path-based methods [8, 33, 36] (3) Hybrid methods [18, 24, 28] combine the above two categories and learn”
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020, unknown): “embedding technology, matrix factorization [86], [87], [88],”