docs: def calculate_recommendations(output_filename, model_name="als"):
      """ Generates artist recommendations for each user in the dataset """
      # train the model based off input params
      artists, users, plays = get_lastfm()
  
      # create a model from the input data
      model = get_model(model_name)
  
      # if we're training an ALS based model, weight input for last.fm
      # by bm25
      if issubclass(model.__class__, AlternatingLeastSquares):
          # lets weight these models by bm25weight.
          logging.debug("weighting matrix by bm25_weight")
          plays = bm25_weight(plays, K1=100, B=0.8)
  
          # also disable building approximate recommend index
          model.approximate_similar_items = False
  
      # this is actually disturbingly expensive:
      plays = plays.tocsr()
  
      logging.debug("training model %s", model_name)
      start = time.time()
      model.fit(plays)
      logging.debug("trained model '%s' in %0.2fs", model_name, time.time() - start)
  
      # generate recommendations for each user and write out to a file
      start = time.time()
      user_plays = plays.T.tocsr()
      with tqdm.tqdm(total=len(users)) as progress:
          with codecs.open(output_filename, "w", "utf8") as o:
              for userid, username in enumerate(users):
                  for artistid, score in model.recommend(userid, user_plays):
                      o.write("%s\t%s\t%s\n" % (username, artists[artistid], score))
                  progress.update(1)
      logging.debug("generated recommendations in %0.2fs",  time.time() - start)