Some questions about the optimization process

[zyang1580]

Hi, I am reading your paper and codes, it is very interesting work and I would like to take your work as a baseline. But I have some questions about the implementation.

(1) when updating the filters, it seems that there are two goals -- minimize the recommendation loss and maximize the classification loss. In your paper, a hyperparameter $\lambda$ is used to combine the two losses together to realize the goals. But in your codes, it seems that the two corresponding losses are optimized separately, and the classification is only updated one batch as following. Which one should I make use of?

for user_batch, rating_batch, item_batch in train_loader: 
        user_batch = user_batch.cuda()
        rating_batch = rating_batch.cuda()
        item_batch = item_batch.cuda()
        d_g_l_get = model(copy.deepcopy(pos_adj),user_batch,rating_batch,item_batch)
        # d_g_l_get = model(copy.deepcopy(pos_adj),copy.deepcopy(pos_adj),user,item_i, item_j) 
        _,f_l,d_l = d_g_l_get 
        loss_current[0].append(f_l.item()) 
        # loss_current[1].append(d_l.item())  
        f_optimizer.zero_grad()
        f_l.backward()
        f_optimizer.step()
        # continue

    d_g_l_get =  model(copy.deepcopy(pos_adj),user_batch,rating_batch,item_batch)
    _,f_l,d_l = d_g_l_get 
    loss_current[1].append(d_l.item())  
    f_optimizer.zero_grad()
    d_l.backward()
    f_optimizer.step()

(2) if the prediction is not computed by inner product instead of neural networks(NN), do I need to update the NN together when I update the filters?

Looking forward to your reply!

[newlei]

This is value function of FairGo.

This is a minimax game like GAN. Therefore, we adopt the method of alternating and two-step optimization.

For the second question, the two updates~(updating the NN and updating the filters ) also are separate.