Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Any sources for the click model based relabeling mentioned in the readme #63

Open
shanmukh98 opened this issue Jul 31, 2023 · 2 comments

Comments

@shanmukh98
Copy link

Was going through the readme and found this
image
The click model that is mentioned here seems interesting
any references for the method mentioned here would be great.

@kretes
Copy link
Contributor

kretes commented Aug 1, 2023

If you're asking about some literature sources about click models in LTR in general, then look at Seq2Slate paper https://arxiv.org/pdf/1810.02019.pdf (mentioned in https://github.com/allegro/allRank/blob/master/allrank/click_models/cascade_models.py#L37C51-L37C83) which explains a procedure of using a reasonable click to obtain click-through data programatically.

This click model is implemented as well as some dummy click models like random-click, and as well as some raw ideas about some more complex click-models

@mhsyno
Copy link

mhsyno commented Aug 1, 2023

All the click models implemented are located in click_models module with some short explanation in docstrings. As mentioned in the part of readme you’ve referred to, before applying a click model you need to pass previously trained allRank model and pass it to rank_and_click.py script with its config modified by specifying click model (along with its arguments) you want to apply - that is because some click models (e. g. cascade model) need relevance (output of the model) to decide which item to click.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants