[Question] How I can access user-items predictions and actuals?

I’m trying to understand how to get all users from my test dataset with the corresponding predicted items per each user? I use RecVAE model and when I get top items like that:

def run_predict(model, test_data, device, k):
    item_tensor = test_data.dataset.get_item_feature().to(device).repeat(test_data.step)
    tot_item_num = test_data.dataset.item_num
    topk_list = []
    for batched_data in test_data:
        interaction, history_index, *_ = batched_data
        try:
            scores = model.full_sort_predict(interaction.to(device))
        except NotImplementedError:
            new_inter = interaction.to(
                device).repeat_interleave(tot_item_num)
            batch_size = len(new_inter)
            new_inter.update(item_tensor[:batch_size])
            scores = model.predict(new_inter)
        scores = scores.view(-1, tot_item_num)
        scores[:, 0] = -np.inf
        if history_index is not None:
            scores[history_index] = -np.inf
        topk_list.append(torch.topk(scores, k=k)[1])
    topk_items = torch.cat(topk_list, dim=0).unique()
    topk_items = topk_items.cpu().numpy()

    return topk_items

it returns just items which doesn’t correspond to number of users I have in test_data.dataset.user_num So I’m a little lost, how would I see user per each of these topk_items predictions? And how to access initial items for each user from test data?

[AsyaEvloeva]

thank you for this example!! now i get it😁

I’m looking not for a cold start problem solution, but more about recommendations for new users with provided interaction records. so if i’m using RecVAE model, is there any already-built possibility to update dataset with such new records and continue training model and making predictions on such updated dataset?

[Sherry-XLL]

@AsyaEvloeva Sorry, we don’t have such an interface at present.