Clustering-Based Collaborative Filtering for Link Prediction

Authors

  • Xiangyu Wang State University of New York at Buffalo
  • Dayu He State University of New York at Buffalo
  • Danyang Chen State University of New York at Buffalo
  • Jinhui Xu State University of New York at Buffalo

DOI:

https://doi.org/10.1609/aaai.v29i1.9162

Keywords:

Link Prediction; Compressed Sensing; Matrix Completion; Collaborative Filtering

Abstract

In this paper, we propose a novel collaborative filtering approach for predicting the unobserved links in a network (or graph) with both topological and node features. Our approach improves the well-known compressed sensing based matrix completion method by introducing a new multiple-independent-Bernoulli-distribution model as the data sampling mask. It makes better link predictions since the model is more general and better matches the data distributions in many real-world networks, such as social networks like Facebook. As a result, a satisfying stability of the prediction can be guaranteed. To obtain an accurate multiple-independent-Bernoulli-distribution model of the topological feature space, our approach adjusts the sampling of the adjacency matrix of the network (or graph) using the clustering information in the node feature space. This yields a better performance than those methods which simply combine the two types of features. Experimental results on several benchmark datasets suggest that our approach outperforms the best existing link prediction methods.

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Published

2015-02-09

How to Cite

Wang, X., He, D., Chen, D., & Xu, J. (2015). Clustering-Based Collaborative Filtering for Link Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9162