A survey of collaborative filtering based social recommender systems

By Xiwang Yang, Yang Guo, Yong Liu, Harald Steck. Computer Communications, 2014

498 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/5afc33eeb7bbd65e4d24f3824a05a9f35502add6

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. Jia Guo
4. Aminu Da’u
5. Ing-Ray Chen
6. Jia Wu
7. Naomie Binti Salim
8. Ivor Wai-Hung Tsang

In Computer Vision and Pattern Recognition

1. Aminu Da’u
2. Jia Wu
3. Naomie Binti Salim
4. Ivor Wai-Hung Tsang
5. Guodong Long
6. Ghulam Mustafa
7. Chengqi Zhang
8. Zhendong Niu

In Information Systems

1. Guodong Long
2. Jia Guo
3. Aminu Da’u
4. Ing-Ray Chen
5. Jia Wu
6. Naomie Binti Salim
7. Ivor Wai-Hung Tsang
8. Ghulam Mustafa

Citations in the corpus

• Trust Management for SOA-Based IoT and Its Application to Service Composition (2016, methodology): “In particular, for recommendations from others, we utilize the design concept of distributed collaborating filtering [12, 38] to select trust feedback from nodes sharing similar social interests.”
• A Survey of Graph Neural Networks for Social Recommender Systems (2022, background): “2 Related Surveys Most of the existing surveys, which fully cover SocialRS papers, focus either on traditional methods [7, 14, 67, 75, 85, 109, 114] (e.”
• Inferring Latent Network from Cascade Data for Dynamic Social Recommendation (2016, background): “The general idea of matrix factorization is to model the user-item interactions with factors representing the latent characteristics of the users and items in the system, like the preference class of users and the category class of items [31].”
• Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “Yang et al. (2014) reviewed studies on collaborative filtering based social recommender systems and classified CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches.”
• Recommender Systems (2016, unknown): “Overviews of trust-based recommender systems may be found in [221, 588, 616, 646].”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “In practice, CF techniques generate recommendation by leveraging the relationship of either inter-user or inter-item (Yang et al. 2014).”