recommend.py

import torch
from torch import nn
import torch.nn.functional as F
class Recommend(nn.Module):
    def __init__(self, num_items, num_users, dims):
        super().__init__()
        self.user_embed = nn.Embedding(num_users, dims)
        self.item_embed = nn.Embedding(num_items, dims)
        self.net = nn.Sequential(
            nn.Linear(2 * dims, 4 * dims),
            nn.LeakyReLU(),
            nn.Linear(4 * dims, 1),
            nn.Sigmoid()
        )
    def forward(self, x):
        f = torch.cat((self.user_embed(x[:, 0]), self.item_embed(x[:, 1])), dim=1)
        return self.net(f)

r = Recommend(12, 1000, 512)

x = torch.tensor([[0, 0], [0, 1], [1, 0]])
y = torch.tensor([1, 0, 1]).float()

# user 0 likes item 0
# user 0 dislikes item 1
# user 1 likes item 0

output = r(dataset)
loss = F.binary_cross_entropy(output, y)
loss.backward()

# run above in training loop

r(torch.tensor[[1, 1]])

# predict if user1 likes item 1

cluster(r.user_embed.weight)
cluster(r.item_embed.weight)
# cluster user and item embeddings to find similar users and items