predict_next_batch not considering other products in the same session about gru4rec HOT 2 CLOSED

batch_size=5
#session_ids = ['0','1','2','3','4']
session_ids = ['0','0','0','0','0']
input_item_ids = valid.ItemId.values[0:batch_size]
predict_for_item_ids = None

print('session_ids: {}'.format(session_ids))
print('input_item_ids: {}'.format(input_item_ids))
print('predict_for_item_ids: {}'.format(predict_for_item_ids))

preds = gru.predict_next_batch(session_ids, input_item_ids, predict_for_item_ids, batch_size)
preds.fillna(0, inplace=True)
print('Preds: {}'.format(preds))

session_ids: ['0', '0', '0', '0', '0']
input_item_ids: ['44790602' '32528001' '32528400' '32528400' '32528010']
predict_for_item_ids: None

Preds: 0 1 2 3 4
04133139 9.912187e-05 2.520136e-04 9.875565e-04 9.875565e-04 1.449896e-04
15994400 1.708450e-05 3.565932e-05 1.420142e-04 1.420142e-04 7.520352e-06
17418001 3.279215e-06 7.654255e-07 1.834371e-06 1.834371e-06 1.035390e-06
15994700 3.367049e-05 1.172445e-04 3.229178e-04 3.229178e-04 3.718351e-05

from gru4rec.

All items in the same session are considered, but the last item has the most impact on the recommendations (especially on the first few places). But earlier items also have some influence and can change the order (and content) of recommendations. However, if there are sharp changes in the session, the model will probably decide to fully ignore previous items, either because this is also supported by the data (e.g. sharp changes is sessions are usually permanent) or because this type of change in focus has never (or rarely) happened in the data and thus during inference time this is the best approach.

from gru4rec.