Re: Recommendation return score more than 5

Thanks for the explanation, Pat! Do you know how to tell the template to use explicit ratings? If so, please let us know how to do that. Thanks!! On 22 December 2017 at 20:33, Pat Ferrel wrote: > I did not write the template you are using. I am trying to explain what

Re: Recommendation return score more than 5

I did not write the template you are using. I am trying to explain what the template should be doing and how ALS works. I’m sure that with exactly the same data you should get the same results but in real life you will need to understand the algorithm a little deeper and so the pointer to the

Re: Recommendation return score more than 5

But I strictly followed the instructions from the site and did not change anything even. Everything I did was steps from this page. I did not perform any additional operations, including editing the source code. Instruction (Quick Start - Recommendation Engine Template):

Re: Recommendation return score more than 5

Implicit means you assign a score to the event based on your own guess. Explicit uses ratings the user makes. One score is a guess by you (like a 4 for buy) and the other is a rating made by the user. ALS comes in 2 flavors, one for explicit scoring, used to predict rating and the other for

Re: Recommendation return score more than 5

I wanted to use the Recomender because I expected that it could predict the scores as it is done by MovieLens. And it seems to be doing so, but for some reason the input and output scale is different. In imported scores, from 1 to 5, and in the predicted from 1 to 10. If by implicit scores you

Re: Recommendation return score more than 5

But we were talking about the Recommender Engine Template, not the UR. I believe the recommender engine template is supposed to predict ratings. However, it doesn't seem to be doing so. On 18 December 2017 at 09:20, Александр Лактионов wrote: > This score is not the same

Re: Recommendation return score more than 5

This score is not the same as in the input. UR calculates LLR between item pairs and leaves only pairs that pass threshold. This is stored in ES as documents with item as id and correlated items tokens as text (item -> (item,item,...)). UR makes a query to es with its own item list (for example,

Re: Recommendation return score more than 5

I didn't solve the problem :( Now I use the universal recommender On 18 December 2017 at 09:12, GMAIL wrote: > And how did you solve this problem? Did you divide prediction score by 2? > > 2017-12-18 10:40 GMT+03:00 Noelia Osés Fernández : > >> I got