Social Collaborative Filtering by Trust

By Bo Yang, Yu Lei, Da-you Liu, Jiming Liu. 2013

442 citations recorded by the source corpus.

How researchers see this paper

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

1. Junliang Yu
2. Hongzhi Yin
3. Haekyu Park
4. Jundong Li
5. Le Wu
6. Hyunsik Jeon
7. Qitian Wu
8. Wenqi Fan

Citations in the corpus

• How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “ularity will perform better on this data. Facebook and Etsy are among websites that use social networks to recommend content. Many algorithms use social network information to improve recommendations [13,22,26,41,42,65] but when training and evaluating these algorithms on data from social platforms,”
• Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, methodology): “CliMF Maximize reciprocal rank method for item ranking [37] LRMF List-wise learning to rank method for item ranking [36] NMF Non-negative matrix factorization [20] PMF Probabilistic matrix factorization [28] SoMF Trust propagation matrix factorization [15] TrMF Trust social matrix factorization [50]”
• Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems (2019, methodology): “ms are rated from 1 to 5 (explicit feedback), as well as the directed trust relationships between users. „e dataset has been widely used as benchmarks for social recommendation. Following other works [10, 16, 31, 36], we randomly choose 80% user-item interactions as training set and the remaining 20”
• TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings (2015, methodology): “Incorporating trust into recommender systems has demonstrated potential to improve recommendation performance (Yang et al. 2013; Fang, Bao, and Zhang 2014), and to help mitigate some well-known issues, such as data sparsity and cold start (Guo, Zhang, and Thalmann 2012).”
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, background): “The common ideas of MF-based social recommendation algorithms can be categorized into three groups: co-factorization methods [22, 46], ensemble methods [20], and regularization methods [23].”
• Graph Neural Networks for Social Recommendation (2019, methodology): “Exploiting social relations for recommendations has attracted significant attention in recent years [27, 28, 37].”
• UniWalk: Explainable and Accurate Recommendation for Rating and Network Data (2017, methodology): “TrustSVD outperforms other methods including PMF [20], RSTE [16], SoRec [17], SoReg [18], SocialMF [10], TrustMF [28], and SVD++ [14].”