Social collaborative filtering for cold-start recommendations

By Suvash Sedhain, Scott Sanner, Darius Braziunas, Lexing Xie, Jordan Christensen. 2014

137 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Statistical and Nonlinear Physics

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. Chuan Shi
2. Philip S. Yu
3. Binbin Hu
4. Scott Sanner
5. Suvash Sedhain
6. Darius Braziunas
7. Wayne Xin Zhao
8. Aditya Krishna Menon

In Management Science and Operations Research

1. Philip S. Yu
2. Chuan Shi
3. Binbin Hu
4. Scott Sanner
5. Suvash Sedhain
6. Darius Braziunas
7. Wayne Xin Zhao
8. Aditya Krishna Menon

In Statistical and Nonlinear Physics

1. Chuan Shi
2. Binbin Hu
3. Wayne Xin Zhao
4. Philip S. Yu
5. Scott Sanner
6. Aditya Krishna Menon

Citations in the corpus

• On the Effectiveness of Linear Models for One-Class Collaborative Filtering (2016, background): “We focus on approaches that rely only on information provided in R , and thus do not discuss approaches that exploit additional information, e.g. those that exploit an exogenous network amongst users or items (Yu et al. 2014; Sedhain et al. 2014).”
• 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.”
• Ieee Transactions on Knowledge and Data Engineering, Manuscropt Id 1 Social Recommendation with Cross-domain Transferable Knowledge (unknown): “Sedhain et al. noted that side information is very important [27].”