Cold users/items support in non-personilized models

[feldlime]

Description

• Supported cold recommendations in non-personalized models (random, popular and popular-in-category)
• Added check for incorrect types of cold targets
• Changed the logic of choosing random sampler for RandomModel and increased the sampling speed
• Changed the logic of RandomModel: now the recommendations are different for repeated calls of recommend methods

Closes #119

Type of change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Optimization

How Has This Been Tested?

Before submitting a PR, please check yourself against the following list. It would save us quite a lot of time.

• Have you read the contribution guide?
• Have you updated the relevant docstrings? We're using Numpy format, please double-check yourself
• Does your change require any new tests?
• Have you updated the changelog file?

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 100.00%. Comparing base (9b3992e) to head (ec1bea6).
Report is 40 commits behind head on main.

❗ Current head ec1bea6 differs from pull request most recent head 72d0eef. Consider uploading reports for the commit 72d0eef to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##              main      #120    +/-   ##
==========================================
  Coverage   100.00%   100.00%            
==========================================
  Files           45        51     +6     
  Lines         2242      2917   +675     
==========================================
+ Hits          2242      2917   +675     

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