Collaborative Knowledge Base Embedding for Recommender Systems

By Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, Wei-Ying Ma. 2016

1,809 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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. Tat-seng Chua
3. Xiang Wang
4. Yongfeng Zhang
5. Philip S. Yu
6. Xing Xie
7. Xu Chen
8. Zhiyuan Liu

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Yongfeng Zhang
5. Xing Xie
6. Xu Chen
7. Meng Wang
8. Fuli Feng

In Recommender Systems

1. Xiangnan He
2. Xiang Wang
3. Xing Xie
4. Yongfeng Zhang
5. Tat-seng Chua
6. Xu Chen
7. Xiaohan Zou
8. Meng Wang

In Knowledge Graphs & Representation

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

Citations in the corpus

• Knowledge Graph Convolutional Networks for Recommender Systems (2019, methodology): “One feasible way is to preprocess the KG by knowledge graph embedding (KGE) methods [20], which map entities and relations to low-dimensional representation vectors [9, 19, 23].”
• Neural Collaborative Filtering (2017, methodology): “…deep learning models for recommendation based on implicit feedback, they primarily used DNNs for modelling auxiliary information, such as textual description of items [38], acoustic features of musics [37, 43], cross-domain behaviors of users [6], and the rich information in knowledge bases [45].”
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, methodology): “• CKE [43] combines CF with structural knowledge, textual knowledge, and visual knowledge in a unified framework for recommendation.”
• DKN: Deep Knowledge-Aware Network for News Recommendation (2018, methodology): “Recently, the knowledge graph has also been used in many applications, such as movie recommendation[52], top-N recommendation [33], machine reading[51], text classification[46] embedding[49], and question answering [10].”
• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “ndle the sparsity problem, we collect two additional types of side information: social relationship among users and profile knowledge of users and celebrities. Our choices are enlightened by [27] and [34], respectively, in which [27] demonstrates that the structural information of social networks ca”
• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “[143] leverage the heterogeneous information in a knowledge base to improve recommendation performance.”
• Neural Snowball for Few-Shot Relation Learning (2019, background): “Knowledge graphs (KGs) such as Word-Net (Miller, 1995), Freebase (Bollacker et al., 2008) and Wikidata (Vrandeˇci´c and Kr¨otzsch, 2014) have multiple applications in information retrieval (Xiong et al., 2017), question answering (Hao et al., 2017) and recommender systems (Zhang et al., 2016).”
• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, methodology): “The collaborative CKE [183] jointly trains KGEs, item’s textual information, and visual content via translational KGE model and stacked auto-encoders.”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “For example, Zhang et al. (2016a) and Lin et al. (2015) integrated AE with CNN to extract visual and textual contents using the word embedding approach for better representation learning to improve the predictive performance of RS. Lee et al. (2016) combined CNN and RNN for quote RS.”
• InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions (2019, background): “…find application in a variety of tasks, such as relation extraction (Mintz et al. 2009; Vashishth et al. 2018), question answering (Bordes, Chopra, and Weston 2014), canonicalization (Vashishth, Jain, and Talukdar 2018), recommender systems (Zhang et al. 2016), and dialog systems (Ma et al. 2015).”
• Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs (2019, background): “…blocks for various AI applications like question-answering [ Zhang et al. , 2018 ] , text classification [ Wang et al. , 2016 ] , recommendation systems [ Zhang et al. , 2016 ] , etc. Knowledge in KGs is usually organized into triples of (cid:104) head entity, relation, tail entity (cid:105) .”
• 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.”
• Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (2019, methodology): “classified 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…”
• Recurrent knowledge graph embedding for effective recommendation (2018, methodology): “The popularity of representation learning (RL) recently prompted a seminal work [44] that exploits KG embedding to capture entity semantics for recommendation.”
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation (2019, methodology): “[30] adopted knowledge base embeddings to generate user and item representations for recommendation, while Huang et al .”
• "Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention (2018, background): “Moreover, identifying symptoms early using social media content can augment proactive and preventative healthcare to the traditional reactive approach [43].”