Multi-relational matrix factorization using bayesian personalized ranking for social network data

By Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme. 2012

201 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics, Probabilistic & Bayesian Methods, Recommender Systems, Signal Processing

How researchers see this paper

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

In Information Systems

1. Philip S. Yu
2. Julian McAuley
3. Chuan Shi
4. Wayne Xin Zhao
5. Yongfeng Zhang
6. Ruining He
7. Wei Lu
8. Chun Chen

In Statistical and Nonlinear Physics

1. Chuan Shi
2. Binbin Hu
3. Wayne Xin Zhao
4. Philip S. Yu
5. Martin Ester
6. Houfeng Wang
7. Qing Zhang
8. Wei Lu

In Probabilistic & Bayesian Methods

1. Philip S. Yu
2. Chuan Shi
3. Binbin Hu
4. Martin Ester
5. Houfeng Wang
6. Qing Zhang
7. Wayne Xin Zhao
8. Chun Chen

In Recommender Systems

1. Ruining He
2. Julian McAuley
3. Philip S. Yu
4. Chuan Shi
5. Binbin Hu
6. Martin Ester
7. Houfeng Wang
8. Qing Zhang

In Signal Processing

1. Chuan Shi
2. Philip S. Yu
3. Binbin Hu
4. Martin Ester
5. Houfeng Wang
6. Qing Zhang
7. Wei Lu
8. Wayne Xin Zhao

Citations in the corpus

• Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, methodology): “, BPR-MF in our experiments). To our knowledge, this is the stateof-the-art framework for the OCCF setting. Recently there have been efforts to extend BPR to incorporate users’ social relations, e.g. [22, 28, 40]. Our model is an extension of BPR-MF to make it fashion-aware while maintaining its acc”
• 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).”
• Social Recommendation with Strong and Weak Ties (2016, background): “Although collaborative filtering approaches can be adapted [24], pairwise ranking methods have gained more traction lately [30, 39, 41, 42, 48].”
• Heterogeneous Information Network Embedding for Recommendation (2017, background): “[22] explain drawbacks of three popular cold-start models [23], [24], [25] and further propose a learning based approach for the cold-start problem to leverage social data via randomised SVD.”
• Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering (unknown): “There are also some work that extends BPR from two dimensions to three dimensions [Rendle and Schmidt-Thieme, 2010], or from one user-item matrix to multiple ones [Krohn-Grimberghe et al. , 2012].”
• Not All Links Are Created Equal: An Adaptive Embedding Approach for Social Personalized Ranking (2016, methodology): “To incorporate rich social information, existing approaches utilize social connections in different ways, but all of those hold the same underlying assumption [1] that given social networks, similar users would prefer similar items .”