Python Recommendation Model Development

I am seeking a talented Python developer to create a recommendation model. The scope involves mining unspecified data types for the model training. It should be noted that while the purpose of the model is to be determined, the data for the model training is in CSV format.

Ideal Skills:
- Expertise in Python.
- Familiarity with recommendation algorithms.
- Experience manipulating CSV data.

Responsibilities:
- Developing a flexible, adaptable recommendation model.
- Importing and managing data from CSV files.
- Advising on and utilizing appropriate algorithms depending on the eventual decision for the purpose of the model.

DELIVERABLES
Output Data Frame:
Attribute Name | Ad 1 | Ad 2 | Ad 3 | Ad 4 | Ad 5 | Ad 6 | Ad 7 | Ad 8 | Ad 9 | Ad 10

ADDITIONAL INFORMATION
We want to recommend 10 ads that users with the same attribute tend to like.
We then want these 10 ads sorted to result in the highest profit (revenue/users).

Will be able to supply a data frame that gives you:

Attribute Name = user cohort
User ID = user id
Ad ID = Advertiser ID
Slot = Where it was, slot = 1 then it’s sorted as the first ad
Revenue = Conversion
Converted = Revenue > 0

Output:
Attribute Name | Ad 1 | Ad 2 | Ad 3 | Ad 4 | Ad 5 | Ad 6 | Ad 7 | Ad 8 | Ad 9 | Ad 10

Where Ad #, where # = slot.


The goal is (ad recommendations based on attributes) based on being given a user id, attribute, slot (this is if the ad was shown 1st, 2nd, 3rd, etc), revenue, and if the user converted

I have the CSV data frame but not much more. did some collaborative filtering with ALS in the past but would love to see what your recommendation would be for it instead as I think it can be done in a much better and cleaner way

data.csv file: https://we.tl/t-uLAbsawEPj

Thank you.