Circle-based recommendation in online social networks

By Xiwang Yang, Harald Steck, Yong Liu. Knowledge Discovery and Data Mining, 2012

402 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/e589c154a94050b5832ec502946b38afc1b20386

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. Guodong Long
2. Chengqi Zhang
3. Yong Liu
4. Xiwang Yang
5. Hongwei Wang
6. Junlin Zhang
7. Chao Liang
8. Hao Ding

In Information Systems

1. Yong Liu
2. Xiwang Yang
3. Hongwei Wang
4. Junlin Zhang
5. Chao Liang
6. Hao Ding
7. Miao Zhao
8. Yang Li

Citations in the corpus

• Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, background): “A common practice in evaluating such approaches is to resort to (i) one [42, 45, 35, 22, 20, 25, 30], sometimes two [39, 31] datasets and (ii) global averages for the metrics of choice.”
• Collaborative Filtering-Based Recommendation of Online Social Voting (2017, methodology): “H. Wang was with the Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.”
• Coupled Matrix Factorization Within Non-IID Context (2014, background): “With the advent of social network, many researchers have started to analyse social recommender systems and various models integrating social networks have been proposed [20].”
• Ieee Transactions on Knowledge and Data Engineering, Manuscropt Id 1 Social Recommendation with Cross-domain Transferable Knowledge (neutral): “Tie strength can refer to homophily [5], circle-based influence [6][7][8], or social trust [9][10].”
• Personalized Recommendation Combining User Interest and Social Circle (2014, neutral): “The CircleCon model [1] has been found to outperform BaseMF and SocialMF [3] with respect to accuracy of the RS.”
• UniWalk: Explainable and Accurate Recommendation for Rating and Network Data (2017, background): “Studies [9, 12, 17, 29] prove that a social network enhances rating prediction.”
• A survey of collaborative filtering based social recommender systems (2014, background): “Recently, authors of [52] proposed circle-based recommendations in online social networks.”
• Inferring Latent Network from Cascade Data for Dynamic Social Recommendation (2016, background): “Numerous social matrix factorization based RSs have recently been proposed to improve recommendation accuracy [33] [19] [32].”