This release contains implementations from paper "Edge-Weighted Personalized PageRank: Breaking A Decade-Old Performance Barrier" (KDD 2015).
Here we use ObjectRank on the DBLP dataset as an illustration.
- numpy (>= 1.6.2)
- scipy (>= 0.10.1)
Download the preprocessed data from here, put it under this folder and decompress it:
tar xvf data.tar.gz
Run the following commands to execute the experiments for query answering and learning to rank with model reduction method:
script/answerquery.sh
script/learnrank.sh
After the execution, the experiemental results will be generated in the result folder.
- Code for preprocessing