Use of social network information to enhance collaborative filtering performance

By Fengkun Liu, Hong Joo Lee. Expert systems with applications, 2010

319 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/cea05127953def98e336084bf88ce7fe790e96ef

How researchers see this paper

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

In Computer Science Applications

1. Yang Guo
2. Hao Ma
3. Irwin King
4. Michael R. Lyu
5. Yong Liu
6. Harald Steck
7. Xiwang Yang
8. Hao Wang

In Information Systems

1. Michael R. Lyu
2. Irwin King
3. Yong Liu
4. Xiwang Yang
5. Hao Ma
6. Harald Steck
7. Charu C. Aggarwal
8. Chao Liu

In Transportation

1. Michael R. Lyu
2. Irwin King
3. Hao Ma
4. Jesús Bobadilla
5. Yang Guo
6. Chao Liu
7. Yong Liu
8. Harald Steck

Citations in the corpus

• A survey of collaborative filtering based social recommender systems (2014, background): “Numerous social matrix-factorization (MF) based RSs have recently been proposed as to improve recommendation accuracy [65, 66, 64, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76].”
• Recommender systems with social regularization (2011, methodology): “There are also a few related work in the literature [7, 18, 23, 37] which studies social recommendation problems.”
• Circle-based recommendation in online social networks (2012, background): “Several social-trust based RSes have recently been proposed to improve recommendation accuracy, to just name a few, [9], [10], [11], [12], [13], [15], [17], [18], and [19].”
• Social Network and Tag Sources Based Augmenting Collaborative Recommender System (2015, background): “Fengkun Liu et al. [16]incorporated social network information into CF and evaluated CF performance with diverse neighbor groups combining groups of friends and nearest neighbors.”
• Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios (2020, unknown): “[38] used the”
• Recommender systems survey (2013, background): “Fengkun and Hong [75] developed a way to increase recommendation effectiveness by incorporating social network information into CF.”
• Personalized Recommendation Combining User Interest and Social Circle (2014, unknown): “Many social network based models [3], [13]–[19], [33]– [37] have been proposed to improve the performance of the RS.”