Need help interpreting research paper about Factorization Machines
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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
Project ID: #19291768