codes.cse.rec's Introduction

for NIPS review

Collaborative Similarity Embedding

Developed Environment

g++ > 4.9 (In macOS, it needs OpenMP-enabled compilers. or try installing lateast version of gcc)

Compilation

$ git clone https://github.com/cnclabs/codes.cse.rec
$ cd codes.cse.rec
$ make

Task

Given a network input net.txt:

userA itemA 3
userA itemC 5
userB itemA 1
userB itemB 5
userC itemA 4

and a input for specifying the fields field.txt:

userA u
userB u
userC u
itemA i
itemB i
itemC i

The model learns the representations of each vertex:

6 5
userA 0.0815412 0.0205459 0.288714 0.296497 0.394043
itemA -0.207083 -0.258583 0.233185 0.0959801 0.258183
itemC 0.0185886 0.138003 0.213609 0.276383 0.45732
userB -0.0137994 -0.227462 0.103224 -0.456051 0.389858
itemB -0.317921 -0.163652 0.103891 -0.449869 0.318225
userC -0.156576 -0.3505 0.213454 0.10476 0.259673

Command Line Interface

Directly call the execution file to see the usage like:

./cli/nemf   # for RATE-CSE
./cli/nerank $ for RANK-CSE

then you will see the options description like:

[CSE]
        command nerank interface for proNet-core

Options Description:
        -train <string>
                Train the Network data
        -save <string>
                Save the representation data
        -field <string>
                Field data
        -dimensions <int>
                Dimension of vertex representation; default is 64
        -sample_times <int>
                Number of training samples *Million; default is 10
        -walk_steps <int>
                Walk steps; default is 5
        -threads <int>
                Number of training threads; default is 1
        -alpha <float>
                Init learning rate; default is 0.025
Usage:

[NERANK]
./nerank -train net.txt -field field.txt -walk_steps 2 -save rep.txt -dimensions 64 -sample_times 10 -alpha 0.025 -threads 1

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