Recurrent knowledge graph embedding for effective recommendation

By Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, Chi Xu. ACM Conference on Recommender Systems, 2018

368 citations recorded by the source corpus.

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/a6a735f8e218f772e5b9dac411fa4abea87fdb9c

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. Yong Li
7. Chen Gao
8. Le Wu

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Xing Xie
5. Meng Wang
6. Yong Li
7. Chen Gao
8. Le Wu

Citations in the corpus

• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “efforts for KG enhanced recommendation can be roughly categorized into three categories: path-basedmodels [168], [174], [175], [176], [177], regularization-based models [132], [169],”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “Specifically, the utilization of knowledge graph with GNN significantly addresses some practical issues in recommender systems [53], for example, cold start problem [42, 232], and dynamicity [163].”
• Graph Neural Networks in Recommender Systems: A Survey (2020, methodology): “Previous works preprocess KG by knowledge graph embedding methods to learn the embeddings of entities and relations, such as [8, 59, 75, 79] or design meta-paths to aggregate the neighbor information [47, 50, 64, 74].”
• Learning Intents behind Interactions with Knowledge Graph for Recommendation (2021, methodology): “GNN-based Methods [17, 38, 39, 41, 47] are founded upon the information aggregation mechanism of graph neural networks (GNNs) [13, 14, 19, 34, 42].”
• Are we really making much progress? A worrying analysis of recent neural recommendation approaches (2019, neutral): “Non-reproducible: KDD: [43], RecSys: [41], [6], [38], [44], [21], [45], SIGIR: [32], [7], WWW: [42], [11]”
• 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 Knowledge Graph-Based Recommender Systems (2020, methodology): “[2], [14], [44], [45], [66], [70], [81], [83], [87], [92], [93], [95], [96] MovieLens-20M [44], [86], [88], [89], [91] museums, parks, cities, etc.) to users based on their historical check-in data.”
• 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 ”
• Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation (2019, methodology): “We consider the recommendation task targeting for implicit feedback like previous efforts [6, 21, 28].”