Social Recommendation Using Low-Rank Semidefinite Program

By Jianke Zhu, Hao Ma, Chun Chen, Jiajun Bu. 2011

25 citations recorded by the source corpus.

The most critical challenge for the recommendation system is to achieve the high prediction quality on the large scale sparse data contributed by the users. In this paper, we present a novel approach to the social recommendation problem, which takes the advantage of the graph Laplacian regularization to capture the underlying social relationship among the users. Differently from the previous approaches, that are based on the conventional gradient descent optimization, we formulate the presented graph Laplacian regularized social recommendation problem into a low-rank semidefinite program, which is able to be efficiently solved by the quasi-Newton algorithm. We have conducted the empirical evaluation on a large scale dataset of high sparsity, the promising experimental results show that our method is very effective and efficient for the social recommendation task.

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

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. Julian McAuley
  2. Ruining He
  3. Yongfeng Zhang
  4. Jure Leskovec
  5. Xiangnan He
  6. Xu Chen
  7. Jiacheng Li
  8. Jiaxi Tang

In Computer Vision and Pattern Recognition

  1. Ruining He
  2. Julian McAuley
  3. Yongfeng Zhang
  4. Xu Chen
  5. Xiangnan He
  6. Philip S. Yu
  7. Zheng Qin
  8. Hongyuan Zha

In Information Systems

  1. Julian McAuley
  2. Ruining He
  3. Yongfeng Zhang
  4. Xiangnan He
  5. Xu Chen
  6. Philip S. Yu
  7. Jiacheng Li
  8. Xue Liu

Citations in the corpus