How to think about weights in Myrrix - machine-learning

Question

I have the following input for Myrrix:
11, 101, 1
11, 102, 1
11, 103, 1
11, 104, 1000
11, 105, 1000
11, 106, 1000
12, 101, 1
12, 102, 1
12, 103, 1
12, 222, 1
13, 104, 1000
13, 105, 1000
13, 106, 1000
13, 333, 1000
I am looking for items to recommend to user 11. The expectation is that item 333 will be recommended first (because of the higher weights for user 13 and items 104, 105, 106).
Here are the recommendation results from Myrrix:
11, 222, 0.04709
11, 333, 0.0334058
Notice that item 222 is recommended with strength 0.047, but item 333 is only given a strength of 0.033 --- the opposite of the expected results.
I also would have expected the difference in strength to be larger (since 1000 and 1 are so different), but obviously that's moot when the order isn't even what I expected.
How can I interpret these results and how should I think about the weight parameter? We are working with a large client under a tight deadline and would appreciate any pointers.

Answer 1

It's hard to judge based on a small and synthetic data set. I think the biggest factor will be parameters here -- what are the # of features? lambda? I would expect features = 2 here. If it's higher I think you quickly over-fit this and the results are mostly the noise left over from that after it perfectly explains that user 11 doesn't interact with 222 and 333.
The values are quite low, suggesting both of these are not likely results, and so their order may be more noise than anything. Do you see different results if the model is rebuilt from another random starting point?