(II. Mitigating the Bias) Learning to Rank (Rerank) the Recommended Team Members to Mitigate Popularity Bias

[hosseinfani]

This is the second step of mitigating the popularity bias using a reranking method.

[Rounique]

In order to have a more fair recommendation and form teams with members from different levels in work( a new researcher for example with a reasonable qualification compared to a more experienced person gets less chance to be recommended) we need to solve the long-tail problem. That means we need to present more long-tail items (people in dblp dataset) since these are the ones who are less likely to be chosen as a team member but might be qualified. Otherwise, we will have a few top researchers who are being recommended most of the time.

[Rounique]

I'm still not sure if we can apply what they have done in this paper to the team formation popularity bias. Because they firstly measure gender and age bias in the recommendation and then present algorithms for computing fairness-aware re-ranking.
So I am trying to figure out how we can relate these two.

[Rounique]

Read these two papers again and made changes in our proposed method document accordingly.

1. Controlling Popularity Bias in Learning-to-Rank Recommendation
2. Managing Popularity Bias in Recommender Systems with Personalized Re-ranking

[Rounique]

Adding Skew measurement for evaluating bias in the recommendation.
A negative SKEWai @k indicates that candidates with value ai are underrepresented in the top k outcomes, whereas a positive SKEWai @k indicates that such candidates are favoured.

[Rounique]

Updating the readme file and completing it.

[hosseinfani]

@Rounique where do you do these changes?

[Rounique]

@hosseinfani We wanted to push it after discussing it. But it is added now.

[hosseinfani]

@Rounique @yogeswarl
you mean the changes here: yogeswarl/fair-team-formation@ee3a3ce

I'm not sure I can see any changes about what you said (skew). Can you?

[yogeswarl]

Hi Dr. @hosseinfani, The skew and infeasible metric were worked. It is the commit done on 14th April

[yogeswarl]

Good evening Dr. @hosseinfani, We are currently trying to run our code on the large dataset. But we keep running into errors, Also I spoke with Karan to get some help regarding the predictions stored in opeNTF to compare results on both our projects which is being currently worked on.
Could you help us by trying to run our code to see if it runs on the DBLP dataset. Our latest work is stored in the dev branch located here. If that does not work, we will rework to see our mistake in loading the files.

thanks,
Yogeswar

[hosseinfani]

Hi @yogeswarl

• Can you post here what errors you face? If the code works on the toy dataset, it should work on the main dataset as they have same file structure. Unless it is related to the load (e.g., oom)
• I'm sorry, I'm busy with the last week of classes and exam preps.

[hosseinfani]

Hi Dr. @hosseinfani, The skew and infeasible metric were worked. It is the commit done on 14th April

As I said, I could not find any lines of change regarding the skew metric. The link I posted is for the 14th April change.

[yogeswarl]

Hi Dr. @hosseinfani, The skew and infeasible metric were worked. It is the commit done on 14th April

As I said, I could not find any lines of change regarding the skew metric. The link I posted is for the 14th April change.

Thank you very much for your response Dr. @hosseinfani
Below I attach an image that shows what the skew and infeasible metric is .

Explaining further, the Skew metric is the logarithmic ratio of the proportion of candidates having the attribute value among the top K results corresponding to the desired proportion of attribute value.

A negative skew is less than desired, A positive skew is more than desired.

The infeasible index and count is also worked on. They were available from the library.

Infeasible index : number of indices of a list which violate a given criteria
Infeasible count : number of (attribute value and indices) pair of a list which violate a given criteria