I would like to ask for a proposal for a machine learning model that would be suitable for the following problem:

I have a training set where each element of type A corresponds to a certain number of elements of type B (both type A and type B elements are described by certain specific columns), i.e.:

A_1 -> B_1, A_1 -> B_2, A_1 -> B_3

A_2 -> B_4, A_2 -> B_5

A_3 -> B_6 ...

For a new element of type A, I want to select the most matching elements of type B from a certain set and indicate how many percent the elements match each other.

Can the problem formulated in this way be solved by machine learning or should I reformulate it in some way?


1 Answer 1


Yes, you can approach the problem use case using a collaborative filtering method. To make recommendations, collaborative filtering algorithms take into account user preferences or behaviour. The relationship between type A and type B items in your situation is the behaviour or desire.

  • $\begingroup$ From review: can you please add some links to information about collaborative filtering so the OP and other interested people can find out more about this. $\endgroup$
    – Lynn
    Commented Apr 2, 2023 at 11:32
  • $\begingroup$ Hello @Lynn, yes I would recommend these links: 1. Collaborative Filtering for Implicit Feedback Datasets ieeexplore.ieee.org/document/4781121 2. Item-based collaborative filtering recommendation algorithms dl.acm.org/doi/10.1145/371920.372071 $\endgroup$ Commented Apr 7, 2023 at 19:09

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