mindis/lcbm

a fast and lightweight collaborative filtering algorithm for binary ratings

LCBM

a fast and lightweight collaborative filtering algorithm for binary ratings.

If you use LCBM please cite the following paper:

• F. Petroni, L. Querzoni, R. Beraldi, M. Paolucci: "LCBM: Statistics-Based Parallel Collaborative Filtering." In: Proceedings of the 17th International Conference on Business Information Systems (BIS), 2014.

###Hadoop MapReduce:

To run the project on hadoop type the following:

bin/hadoop jar /home/hduser/LCBM_mapreduce.jar train test [options]

Parameters:

• train: the name of the file with the train data
• test: the name of the file with the test data.

Options:

• -k int -> specifies the multiplicative factor for the SE. Default 2.
• -split_token char -> specifies the character that splits the dataset.
• -output1 string -> specifies the name of the first output directory in the hdfs.
• -output2 sting -> specifies the name of the second output directory in the hdfs.

Example

bin/hadoop jar /home/hduser/LCBM_mapreduce.jar ml100k/trace1.base ml100k/trace1.test