ScaNN evaluation gives better metrics than BruteForce evaluation

Following the documentation (https://www.tensorflow.org/recommenders/examples/efficient_serving#evaluating_the_approximation), I am trying to compare the performance the ScaNN evaluation and BruteForce evaluation.

Code for BruteForce evaluation:

item_embeddings = items_data.batch(batch_size).map(model.candidate_model)
# Override the existing streaming candidate source and turn on the compute_metrics
model.task.factorized_metrics = tfrs.metrics.FactorizedTopK(
    candidates=item_embeddings
)
model.compute_metrics = True
# Need to recompile the model for the changes to take effect.
model.compile()
%time results_eval = model.evaluate(cached_test, return_dict=True)
results_eval

which gives me result:
{'factorized_top_k/top_1_categorical_accuracy': 0.0016201249090954661, 'factorized_top_k/top_5_categorical_accuracy': 0.006863438058644533, 'factorized_top_k/top_10_categorical_accuracy': 0.01172381266951561, 'factorized_top_k/top_50_categorical_accuracy': 0.03605514392256737, 'factorized_top_k/top_100_categorical_accuracy': 0.05449511110782623, 'loss': 9786.341796875, 'regularization_loss': 0, 'total_loss': 9786.341796875}

Code for ScaNN evaluation:

scann = tfrs.layers.factorized_top_k.ScaNN(num_reordering_candidates=1000)
scann.index(item_embeddings)
model.task.factorized_metrics = tfrs.metrics.FactorizedTopK(
    candidates=scann
)
model.compute_metrics = True
model.compile()
%time scann_result = model.evaluate(cached_test, return_dict=True)
scann_result

And I get:
{'factorized_top_k/top_1_categorical_accuracy': 0.004065040498971939, 'factorized_top_k/top_5_categorical_accuracy': 0.012725344859063625, 'factorized_top_k/top_10_categorical_accuracy': 0.019765524193644524, 'factorized_top_k/top_50_categorical_accuracy': 0.05107811838388443, 'factorized_top_k/top_100_categorical_accuracy': 0.07484976947307587, 'loss': 9786.341796875, 'regularization_loss': 0, 'total_loss': 9786.341796875}

Is there any good reason that the ANN approach is performing better than a BruteForce approach?