Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering

By Tong Zhao, Julian McAuley, Irwin King. 2014

398 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, Graph Learning, Recommender Systems

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Meng Wang
3. Tat-seng Chua
4. Julian McAuley
5. Richang Hong
6. Le Wu
7. Hongzhi Yin
8. Ruining He

In Information Systems

1. Xiangnan He
2. Julian McAuley
3. Meng Wang
4. Hongzhi Yin
5. Tat-seng Chua
6. Xiang Wang
7. Le Wu
8. Yongfeng Zhang

In Management Science and Operations Research

1. Xiangnan He
2. Julian McAuley
3. Wang-Cheng Kang
4. Ruining He
5. Meng Wang
6. Hongzhi Yin
7. Le Wu
8. Richang Hong

In Graph Learning

1. Xiangnan He
2. Jian Tang
3. Ruining He
4. Jundong Li
5. Meng Wang
6. Tina Eliassi-Rad
7. David M. Blei
8. Wang-Cheng Kang

In Recommender Systems

1. Xiangnan He
2. Julian McAuley
3. Meng Wang
4. Ruining He
5. Richang Hong
6. Le Wu
7. Wang-Cheng Kang
8. David M. Blei

Citations in the corpus

• VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, methodology): “More recently BPR-MF has been extended to accommodate both users’ feedback and their social relations (Krohn-Grimberghe et al., 2012; Pan and Chen, 2013; Zhao, McAuley, and King, 2014).”
• Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation (2016, methodology): “The next dataset is collected from a popular online consumer review website Epinions.com by the authors of [27].”
• The Limits of Popularity-Based Recommendations, and the Role of Social Ties (2016, background): “Several other works show or propose that recommendations take place over some social network structure [16, 17, 33, 15, 34].”
• Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019, background): “Others studied merely social inuences [4, 24, 41].”
• Social Recommendation with Strong and Weak Ties (2016, background): “In a nutshell, the core idea is to learn a personalized ranking for each user based on the assumption that a user prefers an observed item over all non-observed items.”
• Translation-based Recommendation (2017, methodology): “4 Œis dataset was collected by [30] from Epinions.”
• A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015, methodology): “[36] incorporates the network into pairwise ranking methods.”
• A Neural Influence Diffusion Model for Social Recommendation (2019, methodology): “SBPR model is proposed into the pair-wise BPR model with the assumption that users tend to assign higher ratings to the items their friends prefer [43].”
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, methodology): “Besides, there are also studies using socially-aware MF to model point-of-interest [48, 51, 52], preference evolution [39], item ranking [55, 61], and relation generation [11, 57].”
• UniWalk: Explainable and Accurate Recommendation for Rating and Network Data (2017, background): “[33] trains ranking-based model with a social network.”
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation (2020, background): “Instead of the social regularization term, some researchers argued that the social network provides valuable information to enhance each user’s behavior [44], [13].”
• A Generic Coordinate Descent Framework for Learning from Implicit Feedback (2016, background): “Both approaches are considered state-of-the-art and while CD outperforms BPR on certain datasets [8, 25, 15, 22, 26], BPR has been shown to work better on others [4, 17, 16, 8].”