Added [start quote] hashed [end quote] cross features

Signed-off-by: David Davó <david@ddavo.me>

recommenders/models/wide_deep/wide_deep_utils.py

@@ -13,6 +13,7 @@
 class WideAndDeepHyperParams:
     user_dim: int = 32
     item_dim: int = 32
+    crossed_feat_dim: int = 1000
     dnn_hidden_units: Tuple[int, ...] = (128, 128)
     dnn_dropout: float = 0.0
     dnn_additional_embeddings_sizes: dict[str, Tuple[int, int]] = field(default_factory=dict)
@@ -53,15 +54,12 @@ def __init__(
 
         self.deep = nn.Sequential(*layers)
 
-        # P(Y=1|X) = W*wide + W'*a^(lf) + bias
-        # which is eq. to W"*cat(wide, a^(lf))+bias
-        wide_input = num_items # TODO: cross product
-        wide_output = num_items
+        # Cross product of users-items
+        exclusive_wide_input = hparams.crossed_feat_dim
 
-        print('wide_input:', wide_input, 'wide_output:', wide_output, 'total:', wide_input*wide_output)
-        print('wide_input:', wide_input, 'prev_output:', prev_output, 'total:', wide_input+prev_output)
         self.head = nn.Sequential(
-            nn.Linear(wide_input+prev_output, wide_output),
+            # Output is binary score. 1 iif item-user pair is a good recommendation.
+            nn.Linear(exclusive_wide_input+prev_output, 1),
             nn.Sigmoid(),
         )
 
@@ -72,17 +70,16 @@ def forward(
         continuous_features: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         users, items = interactions.T
+
         all_embed = torch.cat([
             self.users_emb(users), # Receives the indices
             self.items_emb(items),
             *[ emb(additional_embeddings[k]) for k, emb in self.additional_embs.items() ]
         ], dim=1)
 
-        # The cross-feature is really only the items because there is no
-        # impression data??
-        # https://datascience.stackexchange.com/a/58915/169220
-        cross_product = torch.zeros([items.numel(), self.n_items])
-        cross_product[torch.arange(items.numel()), items] = 1
+        # TODO: Use hashing to avoid problems with biased distributions
+        cross_product_idx = (users*self.n_items + items) % self.hparams.crossed_feat_dim
+        cross_product = nn.functional.one_hot(cross_product_idx, self.hparams.crossed_feat_dim)
 
         if self.hparams.dnn_cont_features > 0:
             deep_input = torch.cat([continuous_features, all_embed], dim=1)