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Need help interpreting research paper about Factorization Machines

There is a method of content recommendation called "Field Aware Factorization Machines" which is explained in this research paper: [login to view URL]~cjlin/papers/[login to view URL]

About this I need something very specific explained: The model.

Using an ffm implentation called xLearn ( [login to view URL] ) I have managed to train the model. However I don't understand how to interpret the trained model file. This file is about 8 mb large and is uploaded in the following link:

[login to view URL]

Fragments of this file are like so:

bias: -1.19493

i_0: 0.222636

i_1: 0

i_2: 0

i_3: 0

i_4: 0

i_5: 0

i_6: 0

i_7: 0

i_8: 0

i_9: 0

i_10: 0

i_11: 0

i_12: 0

.

.

.

v_9990_5: 0.163983 -0.00435618 0.205937 0.162927

v_9990_6: 0.0364918 0.181211 0.136226 0.0891592

v_9990_7: 0.0924703 0.307023 0.271298 0.156904

v_9990_8: 0.0628079 0.250727 0.0637604 0.294064

v_9990_9: 0.31339 0.3204 0.0064398 0.23125

v_9990_10: 0.123403 0.323897 0.200116 0.22379

v_9990_11: 0.0808216 0.32948 0.0250665 0.257791

v_9990_12: 0.322103 0.0737792 0.0105526 0.293231

v_9990_13: 0.315756 0.298412 0.310376 0.0305769

v_9990_14: 0.0390615 0.0692963 0.019608 0.145432

v_9990_15: 0.119959 0.0367788 0.254127 0.0489978

v_9990_16: 0.100716 0.216424 0.00206306 0.091204

v_9990_17: -0.00010065 0.19462 0.120955 0.0980957

I need someone to explain to me what this values are and how are they used. Please interpret the research paper and show me how to understand this.

Thanks!

Michel

Skills: Machine Learning (ML)

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Hi, I am researcher from Indian Institute of science Bangalore. I work on standard machine learning and deep learning. I am aware of FM and FFM (Field Aware Factorization Machines). I have not used XLearn before but i More

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