search

recommenders-team / recommenders

Best Practices on Recommendation Systems
https://recommenders-team.github.io/recommenders/intro.html
MIT License
18.89k stars 3.07k forks source link

Update README #474

Closed miguelgfierro closed 5 years ago

miguelgfierro commented 5 years ago

We have to update the README for the new algos that are in the repo. We also have to update the benchmark results.

@gramhagen could you do VW?

I will do FastAI and NCF

The list of the new algos that we have is:

miguelgfierro commented 5 years ago

@nikhilrj I was looking at the main readme and we don't list in the front page all the algos that we have. I think this is something that the user will like to see quickly.

What are your thoughts on this? what would be a good way to show the algos more clearly?

anargyri commented 5 years ago

I already did that in a new PR #476

gramhagen commented 5 years ago

The 02_models/README is update with VW, are you looking for results that can be read in from the comparison notebook and put into the main /README?

nikhilrj commented 5 years ago

@nikhilrj I was looking at the main readme and we don't list in the front page all the algos that we have. I think this is something that the user will like to see quickly.

What are your thoughts on this? what would be a good way to show the algos more clearly?

Agreed. Maybe let's add an Algorithms section which includes a table that gives a 1 sentence description of each algorithm and which env it supports? Alternatively this info could be another column of the benchmark table.

yueguoguo commented 5 years ago

Agreed. Maybe let's add an Algorithms section which includes a table that gives a 1 sentence description of each algorithm and which env it supports? Alternatively this info could be another column of the benchmark table.

We may also want to make the table look more qualitative instead of quantitative since we want the repo to be non-opinionated. In addition to a one-sentence description, pros and cons, use case scenarios, etc. can also be good things to have.

miguelgfierro commented 5 years ago

good suggestion, I remember an ask for @gcampanella and other was to give a guidance on what recommendation algorithm to use. A recommendation about recommendation :-)

anargyri commented 5 years ago

Agreed. Maybe let's add an Algorithms section which includes a table that gives a 1 sentence description of each algorithm and which env it supports? Alternatively this info could be another column of the benchmark table.

We may also want to make the table look more qualitative instead of quantitative since we want the repo to be non-opinionated. In addition to a one-sentence description, pros and cons, use case scenarios, etc. can also be good things to have.

This is a good point. When updating the Readme, I wrote something like "xDeepFM, good for CTR prediction, works on python + GPU + TF". We could structure this info a bit better e.g. by using a few columns for use case, platform etc.