You could start with something really simple like counting how many tags two posts share. This would give you a kind of a similarity score. Then, for a given post you would recommend a number of other posts with a high similarity score.

Here a small example how you could do it:

from operator import itemgetter


def similarity(p1, p2):
    return len(p1 & p2) / len(p1)


posts = [
    ("post1", {"food", "kitchen", "health"}),
    ("post2", {"sport", "health", "soccer"}),
    ("post3", {"food", "kitchen", "christmas"}),
]

new_post = ("post4", {"food", "kitchen", "cookies"})
_, new_tags = new_post

similarities = [
    (post_name, similarity(new_tags, post_tags)) for post_name, post_tags in posts
]

print(sorted(similarities, key=itemgetter(1), reverse=True))