input: Write a function to get book recommendations based on similarity.
  assert get_recommendations([{'id': 1, 'author': 'Author A', 'title': 'Book 1', 'publisher': 'Publisher X'}, {'id': 2, 'author': 'Author B', 'title': 'Book 2', 'publisher': 'Publisher Y'}]) == [2]
  assert get_recommendations([{'id': 1, 'author': 'Author A', 'title': 'Book 1', 'publisher': 'Publisher X'}, {'id': 2, 'author': 'Author A', 'title': 'Book 3', 'publisher': 'Publisher X'}]) == [2]
  assert get_recommendations([{'id': 1, 'author': 'Author A', 'title': 'Book 1', 'publisher': 'Publisher X'}, {'id': 2, 'author': 'Author B', 'title': 'Book 2', 'publisher': 'Publisher Y'}, {'id': 3, 'author': 'Author A', 'title': 'Book 1', 'publisher': 'Publisher X'}]) == [3]
output: def get_recommendations(books):
      def similarity(book1, book2):
          score = 0
          if book1['author'] == book2['author']:
              score += 1
          if book1['title'] == book2['title']:
              score += 1
          if book1['publisher'] == book2['publisher']:
              score += 1
          return score
      
      target_book = books[0]
      max_score = 0
      recommendations = []
  
      for book in books[1:]:
          score = similarity(target_book, book)
          if score > max_score:
              max_score = score
              recommendations = [book['id']]
          elif score == max_score:
              recommendations.append(book['id'])
  
      return recommendations