Source code for, AutoRec, an autoencoder based model for collaborative filtering. This package also includes implementation of RBM based collaborative filtering model(RBM-CF).

• cython
• progressbar
• envoy
• climin

Models are defined in yaml configuration file. Configuration file consists of three sections

• data: In this section, we define data sources and model save path
  • train : path of the training file
  • test : path of the test file
  • save : path for saving the model
• param: In this section, we define network training parameters
  • lamda: list of regularization paramter per each layer
  • max_iter: maximum number of iteration
  • batch_size: size of the batch
  • optimizer: Choice of the optimizer (lbfgs, rprop, rmsprop)
  • reg_bias: whether to regularize bias or not
  • beta: sparsity control parameter
  • num_threads: maximum number of threads to be used while doing some of the matrix operations (set it to number of CPU cores)
• layer: In this section, we define the network architecture. Layers are defined by layer index(starting from 1). Note that, layer index should be defined in ascending order (For eg: 1, 2, 3). Each layer is defined as
  • Layer index:
    • activation: Type of activation function (identity, sigmoid, relu, nrelu, tanh)
    • num_nodes: number of nodes in the given layer
    • type: layer type (input, hidden, output)
    • partial: whether the data in the given layer is partially observed or not (applicable only to input/output nodes)
    • binary : whether to enforce binary coding in the layer or not

First, you will need to build the cython modules. Build cython modules by running

• bash buildCython.sh

Running Autorec model

• cd nn/autorec
• PYTHONPATH=<NNRec_PATH> python learner.py -c <CONF_PATH>

Running RBMCF model

• cd nn/cfrbm
• PYTHONPATH=<NNRec_PATH> python learner.py -c <CONF_PATH>

If you have any queries, please contact me at mesuvash@gmail.com.