Benchmarking my research results with related work's results I have an accepted paper in CS, in which I proposed a new method in data-science related topics (data-intensive analysis, a lot of hyperparameter tuning and design decisions).

The reviewers opinions are positive, the method is clearly described and the results are validated properly. Thus, the conclusions of the work are valid.

However, one of the reviewers wrote that the paper lacks an empirical comparison with other similar works.

From my point of view, comparing the result of my method to the results of the related work is not viable for many reasons, such as the problem I am tackling is slightly different, the type of the dataset I am using is also different. 

So, I see a fundamental difference between my work the related work that makes any comparison really not valid, though both my work and the related work are in the same area.

Any suggestions to improve my paper?
If the paper was accepted, it means that the reviewers and the editor / chair considered it to have sufficient merit for publication. Comments in the reviews are suggestions to improve the paper, but being the author, it is finally up to your discretion as to what is appropriate.

In your case, assuming that you now need to send the final camera ready version for publication, you could update the paper with the reasons you think such a comparison would be invalid. This would also help future readers of your paper who may have the same question.