Bounds to the predicted scores

[jonnylee719]

Hi, I have been trying out your implementation of fpmc and it's been doing great, so thank you! However, my question is on the scores computed using the learnt models. I computed the scores following the item recommendation method shown in the fpmc paper. The scores can be >1. I am uncertain if I should be expecting a score between 0 to 1.
Are there supposed to be some bounds to the predicted scores or the score can be any real number such that an item ranking can be produced? Thanks for any help!

[flaviovdf]

Not sure, exactly what score are you using?

Flavio

On Tue, Nov 8, 2016 at 11:21 AM, jonnyLee [email protected] wrote:

Hi, I have been trying out your implementation of fpmc and it's been doing
great, so thank you! However, my question is on the scores computed using
the learnt models. I computed the scores following the item recommendation
method shown in the fpmc paper. The scores can be >1. I am uncertain if I
should be expecting a score between 0 to 1.
Are there supposed to be some bounds to the predicted scores or the score
can be any real number such that an item ranking can be produced?

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[jonnylee719]

Hey thanks for the quick reply! I attach the screenshots to the mf and fmc formulas and the combined fpmc formula that I used to compute the scores of items.

[flaviovdf]

Hi, I believe that there is no bound on these parameters. Check Figure 5 in
the paper. Parameters are initialized following a Normal(0, 1)

Sorry it took a while to answer.

Flavio

On Tue, Nov 8, 2016 at 12:22 PM, jonnyLee [email protected] wrote:

Hey thanks for the quick reply! I attach the screenshots to the mf and fmc
formulas and the combined fpmc formula that I used to compute the scores of
items.

[image: mf]
https://cloud.githubusercontent.com/assets/8478381/20102619/7fe545a4-a5a5-11e6-8791-e6b3e170a4c7.png

[image: fmc]
https://cloud.githubusercontent.com/assets/8478381/20102618/7fe0ce52-a5a5-11e6-9b0e-4ddbabcef8cf.png

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