ClusCite: effective citation recommendation by information network-based clustering

By Xiang Ren, Jialu Liu, Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, Jiawei Han. 2014

143 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

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. Sebastian Riedel
2. Luke S. Zettlemoyer
3. Hannaneh Hajishirzi
4. Hinrich Schütze
5. Mohit Bansal
6. Yejin Choi
7. Graham Neubig
8. Timo Schick

In Information Systems

1. Charu C. Aggarwal
2. Edouard Grave
3. Aleksandra Piktus
4. Gautier Izacard
5. Ananthram Swami
6. Nitesh V. Chawla
7. C. Lee Giles
8. Patrick Lewis

In Information Retrieval & Search

1. Sebastian Riedel
2. Gautier Izacard
3. Fabio Petroni
4. Patrick Lewis
5. Edouard Grave
6. Timo Schick
7. Lucas Hosseini
8. Aleksandra Piktus

Citations in the corpus

• Improving Wikipedia verifiability with AI (2022, background): “…retrieval and paper/source recommendation have also received attention in the scientific literature domain for many decades (McNee et al., 2002; Ren et al., 2014; Bhagavatula et al., 2018; Chou et al., 2020), albeit with less of a focus on verifiability of existing citations, with citations…”
• Heterogeneous Graph Neural Network (2019, background): “This ubiquity of HetG has led to an influx of research on corresponding graph mining methods and algorithms such as relation inference [2, 25, 33, 35], personalized recommendation [10, 23], node classification [36], etc .”
• Content-Based Citation Recommendation (2018, methodology): “For example, the average number of citations per document in the DBLP dataset is 5, but Ren et al. (2014) filtered out documents with fewer than 10 citations from the test set.”
• WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages (2018, methodology): “There have been several reference recommendation systems (Huang et al., 2015; Caragea et al., 2013; Huang et al., 2014; Tang et al., 2014; Ren et al., 2014; Meng et al., 2013; Huang et al., 2012) for scientific articles but all of them either use the the text of the cited paper or the underlying cit”
• A Neural Probabilistic Model for Context Based Citation Recommendation (2015, background): “Most citation recommendation models fall into two categories: global recommendation (McNee et al. 2002; Strohman, Croft, and Jensen 2007; Nallapati et al. 2008; Bethard and Jurafsky 2010; Kataria, Mitra, and Bhatia 2010; Kucuktunc et al. 2012; Ren et al. 2014) which recommends a list of references f”
• Heterogeneous Network Embedding via Deep Architectures (2015, background): “A significant amount of research has been accomplished on various topics, such as collective classification [37], community detection [44], link prediction [47], social recommendation [35], targeted advertising [22], and so on.”